Unravelling Pathobiological Molecular Mechanisms of T-Cell Acute Lymphoblastic Leukemia

Rui D. Mendes

Rui D. Mendes

The research here described was conducted in the department of Pediatric Oncology/ Hematology at Erasmus Medical Center - Sophia Children’s Hospital, Rotterdam, the Netherlands. The studies described in this thesis were financially supported by Stichting Kinderen Kankervrij (KiKa).

Publication of this thesis was financially supported by:

Cover: Nathan Sawaya. The original picture displays a two metre sculpture named ‘Building Bricks of Life’ made by the artist Nathan Sawaya. “Just as each brick plays an important part in holding the sculpture together, each gene within the human genome helps us understand more about ourselves as humans and how we are all connected” - Nathan Sawaya

Book design: Rui D. Mendes Printed by:

ISBN: XXX-XX-XXXX-XXX-X

Copyright © 2016 by Rui D. Mendes. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without prior permission of the author.

Unravelling Pathobiological Molecular Mechanisms of T-Cell Acute Lymphoblastic Leukemia

Het ontrafelen van pathobiologische moleculaire mechanismes in T-cel acute lymphoblastische leukemie

Thesis to obtain the degree of doctor from

Erasmus University Rotterdam by command of the rector magnificus

Prof.dr. H.A.P. Pols

and in accordance with the decision of the Doctorate Board.

The public defence shall be held on Tuesday December 13, 2016 at 13:30 hours by

Rui Daniel Martins Nunes Mendes born in Lisbon, Portugal

Doctoral Committee:

Promotor: Prof.dr. R. Pieters

Leescommissie: Prof.dr. C.M. Zwaan Prof.dr. F.J. Staal Prof.dr. F.G. Grosveld

Copromotor: Dr. J.P.P. Meijerink

To my beloved ones:

Mariana, Joaquim e André,

and in memory of my cousin João Martins.

6 Contents

Chapter 1 General Introduction & Aims of the Thesis 9

Chapter 2 The relevance of PTEN-AKT in relation to 30 NOTCH1-directed treatment strategies in T-cell acute lymphoblastic leukemia Haematologica. 2016 Sep;101(9):1010-7

Chapter 3 PTEN microdeletions in T-cell acute lymphoblastic 49 leukemia are caused by illegitimate RAG-mediated recombination events. Blood, 2014, 124(4):567-78

Chapter 4 Lentiviral gene transfer into human and murine 77 hematopoietic stem cells: size matters BMC Res Notes. 2016 Jun 16;9:312

Chapter 5 Downregulation of CD44 functionally defines human 88 T-cell lineage commitment Manuscript submitted

Chapter 6 T-Cell Acute Lymphoblastic Leukemia Oncogenes 113 hijack T-Cell Developmental Programs for Leukemogenesis: the arrest of Early T-cell programs by MEF2C, LYL1 or LMO2 Manuscript in preparation

Chapter 7 General discussion & Future perspectives 135

Chapter 8 Summary/ Samenvatting 145 Supplementary data 151 Curriculum Vitae 181 List of Publications 182 Portfolio 183 Acknowledgements 184

Chapter 1

General Introduction & Aims of the Thesis

9 1. General introduction

1.1. Hematopoiesis and leukemia

Daily, each person produces approximately 1011–1012 blood cells to maintain the number of cells in the peripheral circulation.[1] The continuous production of mature blood cells is essential as these cells have a limited lifespan. This renewal process is supported by hematopoietic stem cells (HSC) that are located in the medulla of the bone (bone marrow), especially in the pelvis, femur, and sternum. Although in smaller numbers, they can also be found in umbilical cord blood (UCB) and in peripheral blood. HSCs give rise to all types of mature blood cells (Figure 1) and have the capacity of self-renewal, since upon cellular division some of the daughter cells remain as HSCs.[2] Instead, the other daughters of HSCs differentiate into myeloid and lymphoid progenitor cells that further give rise to the development of all blood cell types. These changes can often be tracked by monitoring the presence of proteins on the surface of the cell, usually determined as Cluster of Differentiation (CD) markers. HSCs express CD34 and lack markers that are lineage-specific, such as the expression of CD3 in T-cells, CD19 in B-cells, CD33 in myeloid and CD56 in NK-lineage cells. The process of maturation from HSC to effector blood cells occurs in the bone marrow and/or secondary lymphoid organs, including thymus and spleen. Following specific stimuli, such as growth factors, chemokines and contact with cells present in different microenvironments, the hematopoietic precursor cell undergoes changes in chromatin organization and gene expression that promote the differentiation of the cell towards a specific cell type, limiting the potential for differentiation into alternative lineages. [2] However, during the process of maturation, the blood precursor cells may acquire mutations, chromosomal rearrangements and epigenetic changes that prompt the cells to uncontrollable proliferation called leukemia.[3] As leukemic cells continue to grow and divide, they eventually outcompete the normal blood cells, which results in defective protection against infections and pathogens, and hemorrhages.[4] Clinically and pathologically, leukemias can be classified in four main categories. The first division is between acute and chronic leukemias. Acute leukemias are the most common forms of leukemia in children, which are characterized by a rapid increase in the number of leukemic cells.[5] Immediate treatment is required due to the rapid progression and accumulation of malignant cells that are released into the bloodstream and spread to other organs. Other symptoms can be manifested according to the tissue occupied; infiltration of the lymph nodes causes lymphadenopathy and infiltration of the central nervous system may provoke headaches. Chronic leukemia is characterized by the excessive

10 accumulation of relatively more mature leukemic cells, and typically it takes months or years to progress. Chronic leukemia mostly occurs in adults. In addition, leukemias can be classified into myeloid or lymphoblastic leukemia, according to the blood lineage of the affected cell. Furthermore, lymphoblastic leukemias can be further divided into B-cell or T-cell lymphoblastic leukemia.

1.2. Acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is the most common type of cancer in children, comprising approximately 25% of all childhood malignancies.[6] Almost three-fourths of childhood leukemia (ages 0-18) is ALL, while the most common form of acute leukemia in adults is acute myeloid leukemia (AML). In the majority of the cases the leukemic blasts are detected on a blood smear. Blood and bone marrow are used for further classification by immuno-phenotyping and genotyping. A lumbar puncture is performed to detect central nervous system involvement. Immunophenotypic analysis of the blasts indicates whether the leukemia is from myeloid (neutrophils, eosinophils, or basophils) or lymphoblastic origin (B- or T-lymphocytes). Genomic analysis is used to determine the underlying genetic abnormalities, which are associated with specific risks for relapse and survival.[6] Treatment protocols are based on combination chemotherapy, including different classes of drugs. Survival rates of ALL patients improved from less than 10% before 1970 to over 80%.[7]

1.3. T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) results from the malignant transformation of lymphoid precursor cells residing in the thymus that are primed towards T-cell development. T- ALL represents about 15% of pediatric ALL cases and 25% of adult ALL cases,[8] and is typically more frequent in males than females. Clinically, T-ALL patients show diffuse infiltration of the bone marrow by immature T-cell blasts, high white blood cells counts, mediastinal masses with pleural effusions, and frequent infiltration of the central nervous system at diagnosis.[9] Despite the advances in our understanding of the molecular genetics of T-ALL, current treatments are mostly based on multi-agent chemotherapy and stem cell transplantation in part of the patients. Although these treatment regimens are highly effective with overall survival reaching 85% on current treatment protocols, they are often associated with severe toxicity and

11 long-term side effects such as cardiomyopathy, bone necrosis, chronic graft versus host disease, infertility or secondary malignancies, impairing the quality of adult life. In addition, about 15% of pediatric T-ALL patients still relapse due to therapy resistance and those cases are associated with very dismal survival perspectives.[5, 10] Therefore, current research efforts are focused on the search for targets that will eventually lead to more effective and less toxic antileukemic drugs. In order to achieve more specific or individualized therapies it is crucial to improve our understanding of the molecular events that lead to the disease, and the mechanisms involved in disease progression and relapse.

1.3.1. T-cell development in the thymus

T-cells can be distinguished between αβ- or γδ-cells based on the composition of the T-cell antigen receptor (TCR). While αβ T-cells comprise the majority (95%) of T-cell populations in lymphoid organs, γδ T-cells only represent a small fraction of T-cells that are mainly located in the gut mucosa. Normal T-cell development is a strictly regulated, multistep process in which lymphoid precursor cells differentiate into functionally diverse T-lymphocyte subsets in the thymus microenvironment. The different checkpoints are orchestrated by diverse transcriptional regulatory networks[11] and transitions between epigenetic states[12] that are triggered through the activation of membrane receptors by signalling molecules present in thymus, such as cytokines, chemokines and ligands from stromal cells. During this fine-tuned developmental process, inappropriate activation of T-ALL oncogenes and loss of tumor suppressor genes in thymic precursors may provoke uncontrolled clonal expansion and T-ALL. To better understand the disease, it is therefore essential to understand the normal T-cell development in the thymus and to distinguish eventual functions of proto-oncogenes in the transcriptional networks that regulate normal developmental progression. Currently, our understanding of normal T-cell development in the thymus is mostly derived from mouse model studies. Based on these studies,[11] Yui and Rothenberg suggest that T-cell development can be divided in three major regulatory phases, which are divided by the T-cell commitment and the β-selection checkpoint. Those phases are named pre- commitment (phase-1), T-cell identity (phase-2) and post β-selection (phase-3), and are characterized by unique gene networks and cellular features (Figure 2). Each phase is characterized by the expression of certain transcription factors that ensure key developmental and consecutive processes, namely the recruitment of progenitor cells to the thymus, their proliferation, the commitment to T-cell lineage, the response to T-cell receptor signals, and

12 finally the restrain to a particular effector programme.[11] In addition, these developmental stages can be divided according to their dependency to NOTCH- and TCR- signalling programs. For instance, in mice cells in phase-1 and -2 do not express a functional TCR and depend on Notch-signalling to proliferate and differentiate, while cells in phase-3 require the expression of a functional TCR rather than NOTCH signalling (Figure 2A).[11] Although the similarity in the core T-cell transcriptional networks that are activated in humans and mouse, there are some species-specific differences in kinetics and function. For instance, there are clear differences in NOTCH activation status between human and mice T-cell development. The highest level of NOTCH activation in humans occurs at T-cell commitment and decreases thereafter, while in mice there is an increase of NOTCH signalling until the cells reach the β-selection (Figure 2A-B). In line with this, while uncommitted early T-cell precursor cells (ETPs) in humans differentiate preferentially into αβ- lineage DP thymocytes after T-cell commitment [13, 14], in mice it only occurs after β-selection stage[15]. The expression of characteristic cell surface markers during T-cell development allows to distinguish the different phases. Thymocytes can be subdivided into double-negative (DN), double-positive, or single-positive (SP) based on the expression of both T-cell markers CD4 and CD8. In mice, the DN stage can be divided into four distinct subsets based on the expression of CD44 and CD25 (Figure 2A). Although the DN stage in humans has not been clearly defined, it is characterized by consecutive acquisition of CD7, CD5, and CD1a (Figure 2B). [16, 17] In addition, the immature single-positive stage that precedes the DP stage is characterized by the expression of CD8 in mice and CD4 in humans. [18, 19]

1.3.1.1. Programs in pre-committed, early T-cell progenitor cells

In phase-1, human thymus-seeding progenitors are DN, express CD34 and CD7 and do not express CD1a. The mouse counterpart is the DN1 stage, when cells are CD44+CD25-. The T-cell specific developmental program is activated when NOTCH-1 receptors on the surface of the lymphoid precursors interact with Notch ligands in the thymic microenvironment. During phase 1 until beginning of phase 2, Notch signalling interacts with a stem cell and/or progenitor cell gene network that is inherited from multipotent precursors, resulting in the downregulation of phase 1‑ restricted genes and the activation of Notch target genes. The most restricted initial subset of phase 1 regulators (GATA2, MEIS1 and HOXA9) is predominantly expressed in the early T-cell precursor cells (ETPs) and is possibly involved in supporting

13 engraftment in the thymus. Compared to ETP genes, expression of genes such as MEF2C, LMO2, SPI1/PU.1, HHEX, BCL11A, GFI1B, ERG and LYL1 are expressed throughout the whole pre-commitment stage (phase-1) and persist until the transition to the commitment phase (phase-2). LMO2, MEIS1, and NMYC are associated with stem-cell like features in early T- cells,[20, 21] similar to ERG and HHEX that are described to contribute for self-renewal of normal and malignant cells.[21, 22] LYL1 is associated with the expression of the growth factor receptor KIT, and the regulatory factor GFI1 that is essential for the generation of ETPs and for the regulation of Notch signalling.[23, 24] Therefore, albeit transient, the expression of those transcription factors is essential for normal T-cell development. The major landmark in the developmental process, which occurs at the transition between phase-1 and -2, is known as T-cell lineage commitment. According to literature the commitment occurs in humans when cells acquire CD1 (Figure 2B), and in mice when KIT and/or CD44 expression is downregulated between DN2a and DN2b stages (Figure 2A). Although expression of CD1a has been used to define human T-lineage commitment,[25] the exact T-cell commitment point has not been clearly defined. Upon commitment, developing cells adopt an intrinsically irreversible T-cell fate that is caused by the “shut down” of transcription factors and gene regulatory networks, impeding cells to differentiate into alternative lineages even under permissive conditions. (reviewed in [26]) This process is initiated by existing ligands in the thymus that support differentiation of progenitor cells in the T-cell lineage. The activation of NOTCH signalling results in upregulation of many T- cell genes and the loss of progenitor-specific gene expression, and therefore cells cannot differentiate into alternative lineages, such as dendritic cell, myeloid cell, NK cell, innate lymphoid cell or mast cell.

1.3.1.2. Programs that promote T-cell commitment

The gene network exhibited in phase-2 is initiated during phase-1, as the activation of NOTCH signalling in ETPs prompts the expression of crucial regulatory transcription factors, such as TCF1 and GATA3.[27, 28] Besides their role in inhibiting phase-1 progenitor specific factors, TCF1 and GATA3 also collaborate with regulators derived from a pre-thymic stage (e.g. E2A, GFI1, RUNX1, MYB and Ikaros) that are essential for T-cell development. TCF1 is a “gatekeeper” of T-cell fate that is directly activated by NOTCH signals and drives the expression of important T-cell specific regulators including GATA3 and BCL11B. [28, 29] GATA3 has critical roles during different developmental stages, namely in early T-cell survival and growth, T-cell

14 lineage commitment, and in T-cell subset diversification that follows TCR expression and antigen stimulation. ([30], reviewed in [31]) Accordingly, GATA3 is responsible to exclude access to B- cell lineage explaining the loss of B-cell potential nearly at the ETP stage, [32] and it is necessary to trigger BCL11B expression during T-cell commitment as well as to prompt the cells for β‑ selection. The transition from phase-1 to phase-2 is especially well characterized in mice, and seems marked by various events: silencing of the majority of phase-1 genes, cells become strictly dependent on NOTCH signalling to survive, proliferation rates are reduced and BCL11B expression is activated.[33] While cells progress to the DN2b stage (equivalent to CD1+ stage in humans), ETS1 and ETS2 are immediately expressed, LEF1 is induced by increased NOTCH signalling, E2A activity is boosted, and the expression of E-dependent and NOTCH-dependent genes (e.g. RAG1, RAG2, PTCRA, TDT and CD3E) is considerably higher.[34] This transition from phase-1 to phase-2 is not well described in human thymocytes, because one of the requirements for T-cell commitment is reduction of NOTCH signalling. However, recent studies indicate that GATA3 is responsible to induce human T-cell commitment by upregulation of T-cell lineage genes, restraining of Notch activity and repressing NK-cell fate. [30] Consistently, Notch target genes that require strong NOTCH signalling are downregulated during T-cell commitment (e.g. NRARP, DTX1), while others such as HES1 and MYC are expressed until the β-selection checkpoint. [30] During phase-2, many cells enter a G1 arrest and RAG-mediated TCR gene recombinations occur until cells successfully rearrange their TCR-β gene. Beyond the key roles at phase-2 gene network, E proteins, NOTCH signalling and BCL11B seem to have a tight collaboration as well. For instance, E proteins are required to promote and sustain NOTCH-1 signalling, which can be antagonized by ID2, an E protein antagonist. [35, 36] In turn, BCL11B may support the phase-2 stage by suppressing ID2 expression.[37] Importantly, the potential of those cells to become αβ instead of γδ T-cells can be related to their distinctive requirement for BCL11B and NOTCH signalling, which is different in mouse and human. In mouse, the inhibition of NOTCH signalling by ID3 or ID2 factors promotes the development of γδ T-cell lineages.[38] For that reason, the impact of BCL11B deletion is especially prominent in αβ T-cells, in contrast to γδ T-cell lineages,[39] and consequently the timing of BCL11B activation and expression is crucial for T-cell lineage commitment. Furthermore, BCL11B is possibly involved in direct repression of KIT, and BCL11B expression itself may be activated by TCF1, RUNX1 and perhaps GATA3, since the BCL11B promoter and a far-distant enhancer contain binding sites for these factors.[40] Although these positive regulators are expressed before BCL11B, perhaps it needs to reach a certain level of combined

15 expression to remove initial DNA methylation and H3K27me3 marks from both the promoter and the enhancer of BCL11B. Contrarily to mouse thymocytes, high NOTCH activation in human thymocytes during phase 2 promotes γδ T cell development, while lower levels of NOTCH activation promote αβ-lineage differentiation. In this case, specific Notch receptor-ligand interactions control human TCR-αβ or -γδ development, as they exhibit different Notch signal strength.[13] At the final stage of phase-2 regulatory activity, the cells are commited to T-cell lineage, α-lineage biased and undergo further TCR-α gene rearrangements.

1.3.1.3. The β-selection program

The transition to phase-3 occurs at the β‑ selection checkpoint, when thymocytes that have not functionally rearranged the TCR-β gene are eliminated. The expression of a functional TCR-β chain activates the phase-3 gene network and promotes growth and differentiation beyond β‑ selection. At the checkpoint, TCRβ proteins assemble at the cell membrane beside TCR complex components that are already expressed during phase-2, namely pre-Tα, CD3 co- receptor and TCRζ. The newly assembled pre-TCR complex stimulates accelerated cell cycle, enlargement of the cells and increased expression of the co-stimulatory molecule CD28.[41, 42] Analysis of TCR arrangements indicates that recombination events occur in the following order during human and mouse T-cell development: TCR-δ, TCR-γ, TCR-β and TCR-α.[43] In recent studies, transplantation of CD34+ cells from patients with severe combined immunodeficiency (SCID) in xenograft mouse models revealed that rearrangement of TCR-γ starts at the DN CD7+ stage. The TCR-β rearrangements are already initiated at the DN CD7+CD5+ stage (before T-cell commitment),[44] which is an earlier stage than the previously suggested immature SP CD4+.[45, 46] In mice, rearrangement of TCR-γ starts at DN1 stage and TCR-β rearrangements start at DN2, but occur mainly at the DN3 stage (after T-cell commitment). [47] Following β‑ selection, cells are able to signal through pre-TCR, which results in loss of NOTCH-dependency (in mice) and downregulation of NOTCH-target genes and IL7R. Ikaros plays a crucial function at this stage, operating as a tumor suppressor gene by suppressing Notch target genes.[48, 49]. As a response to the inhibition of those signaling pathways that promote growth, the cells activate PI3K by becoming highly responsive to chemokine signaling through CXCR4, [50] and therefore initiate a new stage of very rapid proliferation. Until the end of phase-3 and start of the DP stage, cells further increase the expression of ETS1, TCF7, LEF1, ETS2 and TCF12 (canonical HEB). Also, the stable expression of new transcription

16 factors is activated (RORγt, NFAT3, POU6F1 and IKZF3), while others (EGR2 and ID3) are only transiently activated in response to the TCR signal during the rapidly proliferating stage. When cells reach the DP stage, the interaction of RORγt with MYB promotes cell survival through the induction of BCLXL and impairs conventional effector responses such as proliferation and cytokine production.[51] At this stage, the regulatory functions of TCF1 and LEF1 may be altered by specific interactions with β-catenin, which is a mediator of the canonical WNT pathway. This alteration is responsible for the expression of CD4 and CD8, and supports the replacement of the pre-TCR α-chain with a new functionally rearranged TCR α-chain, which yields a complete αβTCR. [52, 53] As a result, DP thymocytes become prepared for the positive and negative selection events. DP αβTCR+ cells that are positively selected on the basis of specific TCR recognition can further differentiate into effector T cell subsets, such as CD4+ T cells, CD8+ T cells, natural killer T (NKT) cells or regulatory T cells.

1.3.2. Genetic aberrations in T-ALL

T-ALL is a heterogeneous disease that arises from the oncogenic transformation of immature thymocytes. Different subtypes of T-ALL have been identified on the basis of genetic aberrations (Figure 3).[54] The genetic aberrations are characterized by chromosomal rearrangements that lead to the aberrant activation of oncogenic transcription factors, including TAL1 and LMO2 (and related family members), TLX1, TLX3, NKX2-1, HOXA, and MEF2C, and also by particular oncogenic fusion proteins that directly activate the HOXA or MEF2C genes.[54-56] We have denoted these chromosomal rearrangements as type A aberrations, because they are generally considered to be the driving oncogenic event associated with unique expression profiles.[55] Based on their gene expression signatures, T-ALLs can be classified into four major subtypes: ETP-ALL, TLX, proliferative, and TALLMO (Figure 4).[56] These subtypes are associated with specific and sequential T-cell maturational arrest.[8, 45, 54] For instance, the immature cluster is associated with a very early arrest (CD34+cells), similar to an early T-cell precursor (ETP) profile.[57] In addition, the TLX cluster includes γδ-lineage arrested cells; the proliferative cluster is associated with a cortical stage arrest (CD1+cells); and the TALLMO cluster consists of cells arrested at a mature stage (CD4+ and/or CD8+cells).[54] The maturational arrest of the cells generates a premalignant condition that facilitates the rapid acquisition of additional genetic hits, which we denoted as type B mutations.[55] These mutations are prevalent among all T-ALL subtypes and do not necessarily occur in the entire leukemic cell population. Instead, they can appear in subclones that are often either selected or

17 lost during disease progression or post-treatment relapse. [58, 59] Type B mutations affect different signal transduction pathways, including the NOTCH1, IL7R-JAK-STAT, RAS-MEK-ERK and PTEN-PI3K-AKT pathways, and are therefore associated with a variety of cellular processes, including survival and proliferation, cell cycle progression, and epigenetic events. The most frequent genetic hit in pediatric T-ALL is the deletion of the CDKN2A locus, which encompasses the p16/INK4A and p14/ARF suppressor genes, and is present in more than 70% of T-ALL cases. In addition, constitutive activation of NOTCH1 signaling is one of the most prominent oncogenic pathway in T-cell transformation, represented in more than 60% of T-ALL cases. However, increasing evidence suggests that some of the signaling pathways are preferentially mutated in certain T-ALL subtypes. This may be related to the natural activation of a certain pathway at the stage where the pre-leukemic clone is arrested. For instance, IL7- Receptor (IL7R) signaling is required for early thymocyte progenitor (ETP) development,[60] and mutations in IL7R—or the downstream molecules JAK or RAS—are prevalent among ETP-ALL and TLX patients.[61, 62](our own observations) Moreover, PTEN inactivation events are preferentially associated with the TALLMO subtype,[63] while NOTCH1 mutations are more prevalent in the TLX cluster, but have a lower incidence in ETP-ALL and TALLMO subtypes.[64] Although strategies targeting oncogenic type A transcription factor complexes are emerging,[65] multiple selective compounds inhibiting pathways that are activated by type B hits are readily available.

1.3.3. The importance of an in vitro culture system to study T-ALL

Although T-ALL genetic signatures provide clues about the primary and cooperating genetic lesions in the multi-step process that drives malignant transformation, in vivo and in vitro models remain valuable tools to investigate the sequential genetic lesions that can initiate and sustain T- ALLs. In this respect, it is crucial to compare human and mouse T cell development not only at the levels of surface marker expression (e.g. CD markers), but also at the requirements of NOTCH and cytokines signalling as well as the underlying transcriptional networks during differentiation. This will assess the conservation of T-cell developmental programs between both species and is also important to understand the relevance of mouse model studies for human T- ALL. While T-cell development has been extensively studied in mice, the restricted availability of human thymus material has limited a detailed insight of human T-cell development. For that reason, research has focused on mimicking the thymus environment using in vitro differentiation

18 cultures starting with hematopoietic stem cells (HSCs) derived from human cord blood or bone marrow. Early studies were performed by seeding of HSCs in lobes of the mouse fetal thymus, cultures that are denoted as fetal thymus organ cultures (FTOCs) and allow intrathymic T-cell development in vitro.[66] Although this system provided important understanding of developmental processes, the capacity to evaluate specific progenitor populations has remained difficult, since this approach only provides a limited number of T cells. In addition, the procedure with FTOCs is a time-consuming and technically demanding method. As an alternative, the OP9 stromal cells co-culture system is available to support T-cell development in vitro. In particular, the expression of the Delta-like 1 ligand on these bone marrow-derived stromal cells from op/op M-CSF deficient mice induces NOTCH signalling in target cells of the hematopoietic lineage.[67] NOTCH signalling results in inhibition of B-cell differentiation while T-cell differentiation is promoted. Umbilical cord blood-derived HSCs in this co-culture system are primed to develop into the T-cell lineage, and the OP9-DL1 system recapitulates in vivo T-cell development as measured by sequential acquisition of surface markers CD7, CD5, CD1a, and the achievement of the CD4, CD8 double-positive (DP) stage.[16, 17] These results establish an efficient and versatile in vitro system for the generation of relatively larger numbers of human T-lineage cells and offer a model system to investigate the factors that influence human T-cell–lineage commitment and differentiation. In addition, this system may be a valuable tool for investigating the sequential genetic lesions that can initiate and sustain T-ALL, as it is easy to manipulate many factors involved in lymphocyte differentiation, including cytokines, Notch signaling pathway and the expression of specific (onco)genes. However, the OP9-DL1 system also has some limitations like any in vitro system. T-cell development can only induce efficient lymphopoiesis until the CD4+ CD8+ DP stage, and does not support positive/negative selection due to lack of proper MHC expression. T-cell development in this system is very sensitive to subtle differences in cytokines and to culture media changes.[68] Also, the effect of thymic epithelial cells (TECs) such as the presentation of different ligands and release of chemokines that create the thymus environment is ignored. Other issues as cell emigration and trafficking cannot be addressed using this system. To address these questions different immunodeficient mice models can be used, namely NOD/SCIDγc−/− (NSG) and Rag2−/−γc−/− strains. Transplantation of human CD34+ HSCs into these conditioned neonatal mice supports multi-lineage human hematopoiesis leading to the production of T cells, B cells and dendritic cells. [69]

2. Aims of the Thesis

The aims of this thesis are:

I. to identify additional PTEN aberrations that may provide survival and proliferation advantage to (pre)-leukemic cells, and understand their clinical impact in T-ALL (Chapters 1 and 2),

II. to optimize a lentivirus system that supports transduction and stable expression of oncogenes in human and mouse HSCs (Chapter 3),

III. to establish an in vitro co-culture system (OP9-DL1) that supports T-cell development from human umbilical cord blood-derived hematopoietic stem cells (HSCs) in order to define distinct human T-cell development stages in relation to the expression of cell surface markers and underlying transcripitonal programs, and in relation to mouse and human in vivo T-cell development (Chapter 4),

IV. to study the impact of ETP-ALL expressed oncogenes (such as MEF2C, LYL1 or LMO2) on human T-cell development (Chapter 5).

3. Short outline of the Thesis

In chapter 2, we review the incidence of inactivating mechanisms of the PTEN tumor suppressor and we discuss relationships to other recurrent aberrations in T-ALL, such as driving oncogenic rearrangements and mutations affecting the NOTCH1, MYC and AKT pathways. We further investigate the role of PTEN aberrations in causing cellular resistance to NOTCH1- directed therapy, suggesting a new hypothesis that explains conflicting data in literature. Finally, we suggest the use of specific signaling pathway inhibitors to prevent resistance to NOTCH- directed therapy in NOTCH1-mutated T-ALL. In chapter 3, we identify microdeletions as an additional and novel mechanism for PTEN inactivation in T-ALL. The breakpoints of these microdeletions are caused by illegitimate RAG-

20 mediated recombination events. The identification of subclonal PTEN microdeletions indicates that RAG activity may be ongoing in (at least part of) the leukemic cell population, and could therefore be responsible for the clonal diversity and/or selection observed in T-ALL during disease progression. The discovery of PTEN microdeletions adds a new level of complexity that should be addressed in the development of future antileukemic strategies for ALL. In chapter 4, we generate a strategy for the cloning, production and transduction of a lentiviral vector with the aim of studying the leukemogenic effects of T-ALL oncogenes during T- cell development. Hence, we optimize the transduction of murine and human HSCs by optimizing both vector design and serum-free virus production as well as by developing a qRT- PCR to quantify viral batches. We suggest that proviral RNA length is an important factor that determines transfection and viral productions as well as transduction efficiencies. In chapter 5, we validate the OP9-DL1 co-cultures as an in vitro system that mimics thymic αβ T-cell lineage development. In particular, we perform gene expression arrays of in vitro differentiated early T-cell subsets at consecutive stages of development. As validation, we compare expression profiles of the in vitro generated T-cell subsets with different stages of human and mouse thymocyte development in vivo. In addition, we define a gene signature that clearly distinguishes two major T-cell differentiation stages, and that correlates with in vivo pre- and post-commitment T-cell profiles. We further demonstrate that loss of CD44 functionally marks human T-cell commitment, which is supported by analysis of TCR-β rearrangements as well as by multi-lineage differentiation experiments. In chapter 6, we show that T-ALL subtypes are strongly associated with consecutive T- cell developmental programs. We demonstrate that immature and TLX subtypes are associated with a pre-commitment program, and the proliferative and TALLMO subtypes with a post- commitment program. Detailed analysis of T-ALL gene expression profiles indicates that T-ALL oncogenes interfere with T-cell developmental programs. Finally, we demonstrate that ectopic expression of typical ETP-ALL oncogenes (such as MEF2C, LYL1 or LMO2) promotes developmental arrest of early thymic progenitor (ETP) cells. In Chapter 7, we discuss all findings presented in the thesis and put these into perspective. In Chapter 8 we summarize the content of the thesis (in English and Dutch).

21 References

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24 60. Fry, T.J. and C.L. Mackall, The many faces of IL-7: from lymphopoiesis to peripheral T cell maintenance. J Immunol, 2005. 174(11): p. 6571-6. 61. Zenatti, P.P., et al., Oncogenic IL7R gain-of-function mutations in childhood T-cell acute lymphoblastic leukemia. Nat Genet, 2011. 43(10): p. 932-9. 62. Zhang, J., et al., The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature, 2012. 481(7380): p. 157-63. 63. Zuurbier, L., et al., The significance of PTEN and AKT aberrations in pediatric T-cell acute lymphoblastic leukemia. Haematologica, 2012. 64. Zuurbier, L., et al., NOTCH1 and/or FBXW7 mutations predict for initial good prednisone response but not for improved outcome in pediatric T-cell acute lymphoblastic leukemia patients treated on DCOG or COALL protocols. Leukemia, 2010. 24(12): p. 2014-22. 65. Filippakopoulos, P., et al., Selective inhibition of BET bromodomains. Nature, 2010. 468(7327): p. 1067-73. 66. DeLuca, D., et al., Programmed differentiation of murine thymocytes during fetal thymus organ culture. J Immunol Methods, 1995. 178(1): p. 13-29. 67. Schmitt, T.M. and J.C. Zuniga-Pflucker, Induction of T cell development from hematopoietic progenitor cells by delta-like-1 in vitro. Immunity, 2002. 17(6): p. 749- 56. 68. Six, E.M., et al., Cytokines and culture medium have a major impact on human in vitro T-cell differentiation. Blood Cells Mol Dis, 2011. 47(1): p. 72-8. 69. Manz, M.G., Human-hemato-lymphoid-system mice: opportunities and challenges. Immunity, 2007. 26(5): p. 537-41.

25 Figure 1. Schematic representation of human hematopoiesis. Multipotent hematopoietic stem cell have self-renewal capacity and differentiate into all blood cell types.

Figure 2. Schematic overview of mice (A) and human (B) αβ T-cell development. The sequential stages from a pre-thymic progenitor to the double-positive (DP) stage are depicted in the figures. The mice developmental stages are accompanied of associated expression of surface markers and transcription factors. The variation in colour intensity represents their level of expression. Obtained from Yui and Rothenberg, Nat Rev Immunol, 2014. 14(8): p. 529-45.

Figure 3. Schematic overview of T-ALL genetic subgroups ETP-ALL, TLX3, TLX1, HOXA and TAL/LMO in relation to their T-cell developmental stage based on EGIL or TCR classification systems. * LMO2 is ectopically expressed in LMO2-rearranged cases but is not included in this figure. Adapted from Meijerink et al, Best Pract Res Clin Haematol, 2010. Sep;23(3):307-18.

Figure 4. Unsupervised hierarchical cluster analysis of 117 pediatric T-ALL samples and 7 normal bone-marrow (NBM) controls based on 435 probesets. Cytogenetic rearrangements indicated are: S, SIL-TAL1; T, TAL1; t, TAL2; O, LMO1; L, LMO2; $, TAL2/LMO1; N, SET- NUP214; C, CALM-AF10; M, MYB; A, Inv(7)(p15q34); 1, TLX1; 3, TLX3; and n, normal bone marrow controls. The samples with the highest TAL1 or LYL1 expression, positivity for TLX1 and TLX3 expression, and expression of the immunophenotypic markers CD13 and/or CD33, CD4 or CD8 are indicated; u, no data available. Obtained from Homminga et al, Cancer Cell, 2011. 19(4): p. 484-97.

Figure 1.

26 Figure 2

Figure 2

28 Figure 3

Figure 4

Chapter 2

The relevance of PTEN-AKT in relation to NOTCH1-directed treatment strategies in T- cell acute lymphoblastic leukemia

Rui D. Mendes1, Kirsten Canté-Barrett1, Rob Pieters1,2 and Jules P. P. Meijerink1

1Department of Pediatric Oncology/Hematology, Erasmus MC Rotterdam-Sophia Children’s Hospital, Rotterdam, the Netherlands, 2Princess Máxima Center of Pediatric Oncology, Utrecht, the Netherlands

Haematologica. 2016 Sep;101(9):1010-7

30 Abstract The tumor suppressor phosphatase and tensin homolog (PTEN) negatively regulates phosphatidylinositol 3-kinase (PI3K)-AKT signaling and is often inactivated by mutations (including deletions) in a variety of cancer types, including T-cell acute lymphoblastic leukemia. Here, we review mutation-associated mechanisms that inactivate PTEN together with other molecular mechanisms that activate AKT and contribute to T-cell leukemogenesis. In addition, we discuss how Pten mutations in mouse models affect the efficacy of gamma-secretase inhibitors to block NOTCH1 signaling through activation of AKT. Based on these models and on observations in primary diagnostic samples of T-cell acute lymphoblastic leukemia patients, we speculate that PTEN-deficient cells employ an intrinsic homeostatic mechanism in which PI3K- AKT signaling is dampened over time. As a result of this reduced PI3K-AKT signaling, the level of AKT activation may be insufficient to compensate for NOTCH1 inhibition, resulting in responsiveness to gamma-secretase inhibitors. On the other hand, de novo acquired PTEN inactivating events in NOTCH1-dependent leukemia could result in temporal, strong activation of PI3K-AKT signaling, increased glycolysis and glutaminolysis, and consequently gamma- secretase inhibitor resistance. Due to the central role of PTEN-AKT signaling and in the resistance to NOTCH1 inhibition, AKT inhibitors may be a promising addition to current treatment protocols for T-cell acute lymphoblastic leukemia.

T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is a cancer of developing T-cells in the thymus. T- ALL is characterized by chromosomal rearrangements. These rearrangements can lead to the aberrant activation of oncogenic transcription factors by placing their genes under the control of promoters and/or enhancers of T-cell receptor genes, the BCL11B gene, or other genes; occasionally, these rearrangements can give rise to oncogenic fusion proteins. The activated oncogenic transcription factors include TAL1 and LMO2 (and related family members), TLX1, TLX3, NKX2-1, HOXA, and MEF2C; in addition, certain oncogenic fusion proteins can directly activate the HOXA or MEF2C genes.(1, 2) Oncogenic proteins facilitate the developmental arrest of pre-leukemic immature T-cells. We previously proposed that these chromosomal rearrangements should be classified as type A aberrations, as they are generally considered to be the driving oncogenic event associated with unique expression profiles.(2) Based upon their gene expression signatures, T-ALL can be classified into the following four major subtypes: ETP-ALL, TLX, proliferative, and TALLMO.(3-5)

31 Maturation arrest induces a pre-leukemic condition in which additional mutations can give rise to T-ALL.(1, 2) These secondary mutations are not necessarily clonal events and are often selected during disease progression or post-treatment relapse.(6, 7) We therefore proposed that these mutations should be classified as type B aberrations.(2) Type B mutations are prevalent among all T-ALL subtypes and affect a wide variety of cellular processes, including survival and proliferation, cell cycle progression, and epigenetic events. Type B mutations often affect signal transduction pathways, including the NOTCH1, IL7R-JAK-STAT, RAS-MEK-ERK, and PTEN- PI3K-AKT pathways. A growing body of evidence suggests that some of these signaling pathways are preferentially mutated in specific T-ALL subtypes, presumably due to the fact that developing T-cells are dependent on these pathways in specific stages. For example, mutations in IL7 receptor (IL7R) and the downstream molecules JAK or RAS are prevalent among TLX and ETP-ALL patients.(8-10) Although new therapeutic strategies that target oncogenic transcription factor complexes are emerging,(11) several compounds that selectively inhibit altered signaling pathways are currently available. Thus, inhibiting signaling proteins such as NOTCH, IL7R, RAS and/or AKT may provide a promising new therapeutic approach for T-ALL. In this review, we describe the role of PTEN as a tumor suppressor and we discuss various PTEN inactivating mechanisms observed in different human cancers and T-ALL. Besides PTEN inactivation, we describe other mechanisms that contribute to AKT activation and leukemogenesis. Finally, we discuss PTEN-AKT signaling in relation to future NOTCH1-directed therapies and provide a rationale for the use of AKT inhibitors in addition to current treatment protocols.

The PTEN tumor suppressor

Mutations in the tumor suppressor gene PTEN (phosphatase and tensin homolog), which is located on chromosomal band 10q23, are highly common among a wide range of cancers.(12, 13) The PTEN gene contains nine exons, and the encoded protein includes an N-terminal phosphatase domain, a central C2 lipid membrane-binding domain, and a C-terminal tail domain

(Figure 1). PTEN is a phosphatase that dephosphorylates PIP3 (phosphatidylinositol (3,4,5)- triphosphate) to produce PIP2 (phosphatidylinositol (4,5)-bisphosphate), thereby opposing the function of PI3K (phosphatidylinositol 3-kinase). PI3K converts PIP2 into PIP3, which in turn activates key downstream kinases, including PDK1 and AKT (Figure 2). Thus, PTEN is an important negative regulator of PI3K-AKT signaling. Because AKT plays key roles in cellular metabolism, proliferation and survival, inactivation of PTEN by genetic aberrations drives

32 survival and uncontrolled proliferation, ultimately leading to cancer.(14) A recent study identified an alternate translation initiation site located upstream of the coding region of canonical PTEN that generates a larger form of PTEN.(15) This isoform is known as PTENα and is described to be involved in mitochondrial energy metabolism.(15)

PTEN aberrations in cancer

Heterozygous germline mutations in PTEN were identified initially in 60-80% of patients belonging to a group of rare syndromes, including Cowden syndrome, Bannayan-Riley- Ruvalcaba syndrome, and PTEN-related Proteus syndrome; these disorders are known collectively as PTEN hamartoma tumor syndrome (PHTS).(16) With respect to sporadic (i.e., non-hereditary) tumors, heterozygous PTEN mutations occur in 50-80% of prostate, glioblastoma, and endometrial cancers and 30-50% of lung, colon, and breast cancers.(17) Loss of both functional PTEN alleles is common among prostate and breast cancer patients, as well as melanoma and glioblastoma patients.(18) The majority of these aberrations are caused by point mutations, small insertions, or deletions, all of which can occur throughout the entire PTEN gene. At the transcriptional and post-transcriptional levels, PTEN inactivation can occur via promoter methylation and through the expression of PTEN-directed microRNAs.(19) PTEN activity is also regulated at the post-translational level: phosphorylation, ubiquitination, oxidation, and acetylation can regulate the phosphatase activity, subcellular localization, and degradation of PTEN.(17) Defects in any of these processes may explain the absence of functional PTEN in cancer patients who apparently lack genetic aberrations in PTEN.(20-22) Several ALL cases have been identified in which high levels of inactive PTEN are accompanied by an active PI3K- AKT pathway.(23, 24) Although the majority of prevalent pathogenic mechanisms affect the loss of one or both PTEN alleles, subtle changes in PTEN protein levels can have a powerful effect on cancer susceptibility and/or tumor progression, as exemplified by the Pten hypomorphic mouse model.(25) Therefore, the level of functional PTEN affects tumor susceptibility, and PTEN function can be compromised at the DNA, mRNA, and/or protein levels.

PTEN aberrations in T-ALL

PTEN deletions and mutations were initially identified in cell lines.(26, 27) Restoring PTEN levels in these cell lines decreases cell size and induced apoptosis by suppressing the PI3K-AKT pathway.(28) Studies by others(29-34) and our group(21, 35) revealed aberrations in the PTEN-

33 PI3K-AKT pathway in approximately 23% of primary samples obtained from pediatric T-ALL patients. With respect to T-ALL subtypes, we have shown that PTEN aberrations are strongly associated with TAL- or LMO- rearranged patients in children (21) and the same was observed in adult T-ALL cohorts.(36) The vast majority of PTEN aberrations include nonsense mutations in exon 7 (which truncate the C-terminal domain) and deletions that affect nearly the entire locus (Figure 1). Although truncated PTEN proteins that lack the lipid-binding C-terminal domain retain their phosphatase activity, they are highly unstable and are degraded rapidly.(37) In mice, truncated PTEN leads to decreased genomic stability and the development of multiple cancers.(38) Recently, we reported that approximately 8% of T-ALL patients have a RAG- mediated microdeletion in the phosphatase domain that disrupts the reading frame (Figure 1).(35) In addition, mutations have been identified in PI3K and AKT; specifically, 9% of pediatric T-ALL patients have a mutation in either the catalytic (PIK3CA) or regulatory (PIK3R1) subunit of PI3K, and 2% of patients have a mutation in AKT itself (Figure 2A).(21, 30) Many T-ALL patients with a heterozygous PTEN mutation also acquire a deletion(21) or microdeletion(35) in their remaining wild-type allele in leukemic subclones(29) that may give rise to relapse. This phenomenon was demonstrated functionally by Clappier et al. who used an elegant human T- ALL xenograft transplantation model in mice and found the selection and preferential outgrowth of PTEN-inactivated leukemic cells.(39). In line with this, heterozygous Pten knockout mice develop T-cell leukemia in which the remaining wild-type allele is frequently deleted.(40-42) Another leukemogenic mechanism that can inactivate PTEN at the protein level is both the increased expression of casein kinase 2 (CK2) or the production of reactive oxygen species (ROS) that stabilize inactive forms of PTEN proteins and lead to impaired phosphatase activity.(23, 43) Together, these findings indicate the existence of ongoing pathogenic pressure to inactivate both PTEN alleles during disease progression, and the resulting loss of PTEN activity in turn activates the PI3K-AKT pathway.

Clinical implications

We have reported that aberrations in PTEN represent a significant, independent risk factor for relapse in T-ALL patients treated using either the DCOG (Dutch Childhood Oncology Group) or COALL (German Cooperative Study Group for Childhood ALL) protocol.(21, 35) Similar results were reported for other pediatric T-ALL patient cohorts treated using other protocols.(32, 33) In the BFM (Berlin-Frankfurt-Munster) study, the presence of NOTCH1-activating mutations in addition to PTEN-inactivating mutations predicts for good outcome similar to patients harboring

34 NOTCH1-activating mutations only,(32) suggesting that NOTCH1-mutations can antagonize the unfavorable effect of PTEN aberrations. In the French GRAALL (Group for Research in Adult ALL) study of adult T-ALL, patients with aberrations in RAS and/or PTEN had significantly worse outcome compared to patients without such mutations.(36) This was not confirmed in the MRC UKALL2003 trial for pediatric T-ALL; RAS and/or PTEN aberrations also did not change the favorable outcome of patients with NOTCH1/FBWX7 mutations.(44) Taken together, these findings suggest that PTEN aberrations may represent a general poor prognostic factor in T- ALL.

NOTCH1 mutations lead to activation of AKT

More than 65% of T-ALL patients have aberrant activation of the NOTCH1 pathway due to mutations either in the NOTCH1 gene itself or in FBXW7, which encodes E3-ubiquitin ligase.(45, 46) Thus, the NOTCH1 pathway may be an ideal target for therapeutic intervention. Furthermore, NOTCH1-directed therapies are clinically important, as they can also boost the cellular response to steroids.(47, 48) Gamma-secretase inhibitors (GSIs), which inhibit the presenilin gamma-secretase complex, block the cleavage of NOTCH1 at its S3 site; this cleavage step is required to release the active, intracellular NOTCH1 domain (ICN1) upon ligand binding (Figure 2B). Several groups have applied GSI to cell lines derived from T-ALL patients with NOTCH1 activating mutations; although GSI treatment initially induces cell cycle arrest, the majority of cell lines adapt and ultimately stop responding to the treatment (i.e., develop GSI resistance).(45, 49) Nevertheless, GSI treatment effectively blocks gamma-secretase activity, resulting in reduced intracellular levels of the ICN1 domain and reduced expression of NOTCH1’s target genes.(29) Therefore, GSI resistance is caused by other mechanisms that circumvent NOTCH1 inhibition.(50, 51) Consistent with this notion, Palomero and co-workers found that decreased PTEN levels in cell lines are correlated with GSI resistance, and GSI- resistant lines have increased levels of activated AKT.(29) Restoring the expression of functional PTEN in these GSI-resistant lines restored a GSI sensitivity response, whereas constitutively activated AKT or using shRNA to knockdown PTEN expression provoked GSI resistance in a GSI-responsive line.(29) This seminal study identified two important NOTCH1 downstream targets that regulate PTEN expression: HES1 and MYC. HES1 is a robust transcriptional repressor, whereas MYC is a weak transcriptional activator. Because the negative effect of HES1 prevails over the positive effect of MYC, PTEN expression is suppressed (Figure 2B).(29)

35 However, GSI resistance by leukemic cells resulted in disappointing results upon testing the GSI inhibitor MK-0752 in clinical trial (DFCI-04-390).(52) This trial was unsuccessful due to the compound’s limited efficacy in leukemic cells and severe gastrointestinal toxicity. To overcome these issues, next-generation NOTCH1 inhibitors with reduced off-target toxicity are currently in development.(53) For example, promising strategies include selectively blocking NOTCH1 using anti-NOTCH1 antibodies(54, 55) or chemically modified peptides that block the NOTCH transcriptional complex in the nucleus.(56)

PTEN is not a priori linked to GSI resistance in human T-ALL

Despite the initial report by Palomero and co-workers,(29) subsequent studies have not confirmed that loss of PTEN activity is intrinsically linked to GSI resistance.(21, 57, 58) For example, GSI sensitivity was similar between NOTCH1-driven T-cell leukemia cells obtained from wild-type mice and from PTEN knockout mice.(58) However, PTEN deficiency does accelerate the disease progression of NOTCH1-driven leukemia.(58) Using a different Pten knockout mouse model (Ptenflox/flox/Lck-Cre), Hagenbeek et al. found that PTEN-deficient thymocytes were just as sensitive to in vitro GSI treatment as wild-type thymocytes.(57) Moreover, several human T-ALL cell lines with mutant alleles of PTEN —other cell lines than those used by Palomero et al.—were actually sensitive to GSI.(21) In TALLMO diagnostic patient samples, PTEN is frequently inactivated in the absence of NOTCH-activating mutations.(21, 32, 36, 59) Thus, mutations in PTEN and NOTCH1/FBXW7 mutations are frequently independent genetic events and only co-occur in a small number of primary patient samples. In those patients that harbor both PTEN mutations and NOTCH1/FBXW7 mutations, the NOTCH1 mutations are usually weakly activating mutations. Because PTEN and NOTCH1 mutations are mostly independent genetic events in primary T-ALL, PTEN-deficient leukemic cells in T-ALL patients likely do not have intrinsic GSI resistance at disease presentation. Perhaps one way that PTEN-deficient T-ALL can be linked to GSI resistance is upon relapse, when NOTCH1-dependent leukemic cells may have lost PTEN activity, possibly due to clonal selection following treatment. However, there is currently no evidence to support this notion. The question remains, is it possible that immediately following PTEN loss, NOTCH1- dependent T-ALL becomes NOTCH1-independent and develops GSI-resistance? Recently, Adolfo Ferrando’s group addressed this intriguing question by generating an elegant mouse model of NOTCH1-induced T-ALL in which the Pten gene is deleted only in established tumors.(60) Unlike previous Pten knockout models,(57, 58) deletion of Pten in this new model

36 conferred strong resistance to dibenzazepine (DBZ), a potent GSI. In this model,(60) Pten loss activated expression of genes involved in cell metabolism, ribosomal RNA processing, and amino acid and nucleotide biosynthesis, genes that are normally suppressed following NOTCH1- inhibiting GSI treatment. Moreover, GSI treatment increased leukemic cells’ dependency on autophagy in order to recycle essential metabolites. Pten loss also relieved the GSI-instigated block of glycolysis and glutaminolysis, a phenotype that was copied by expressing the constitutively active myrisoylated AKT. Because both the GSI DBZ and the glutaminase inhibitor BPTES (bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide 3) act in a synergistic fashion in inducing anti-leukemic effects, the authors proposed glutaminolysis as a major therapeutic target for treating NOTCH-activated T-ALL.(60) An unanswered question remains: why the loss of Pten in an established NOTCH1- driven tumor causes GSI resistance,(60) while NOTCH1-driven tumors that are generated in Pten knockout mice remain GSI-sensitive?(57, 58) The answer may lie in the ability of cells to adapt to PTEN loss by dampening PI3K-Akt signaling over time to a level that is still of advantage to leukemic cells. Unlike progressive reduction in PI3K-Akt signaling, the loss of PTEN may initially drive the rapid, high activation of Akt, resulting in cell proliferation, survival, and GSI resistance. This hypothesis predicts two consequences of GSI or other NOTCH1- inhibiting treatment. First, T-ALL patients who lack PTEN activity at disease onset may still respond to NOTCH1 inhibition. Second, NOTCH1 inhibition may trigger leukemic cells to acquire mutations such as PTEN deletions, which leads to activation of AKT and resistance to NOTCH1 inhibitors. Several key observations provide support for this hypothesis of reduced PI3K-AKT signaling over time in the absence of PTEN. For example, we found no difference in AKT phosphorylation between primary T-ALL patients with aberrant PTEN and patients without such mutations,(21) indicating that patients with PTEN-defective leukemia may have adapted and reduced PI3K-AKT signaling. Reduced AKT activation may explain why primary PTEN-defective T-ALL cells are NOTCH1-dependent and remain GSI-sensitive.(58) Although this hypothesis has not been tested formally, future NOTCH-inhibiting therapies may be more effective when combined with inhibitors of PI3K or AKT. Consistent with this, the PI3K/mTOR dual inhibitor PI- 103 resulted in enhanced NOTCH-MYC activity in T-ALL cell lines.(61) Additionally, T-ALL induced by retroviral insertional mutagenesis in wild-type or RasG12D-mutant mice demonstrated an initial response to PI3K-inhibitor GDC-0941 treatment.(62) However, this treatment led to the survival and outgrowth of drug resistant clones with active PI3K-AKT signaling that frequently had reduced Notch1 signaling.(62) To avoid resistance when combined

37 with NOTCH1 inhibitors, the authors propose a sequential treatment using a NOTCH1 inhibitor at diagnosis to eliminate NOTCH1 mutant clones followed by PI3K/AKT inhibitor treatment.(62)

Other mechanisms that can activate AKT and lead to GSI resistance

Eighty-five percent of T-ALL patients have an activated AKT pathway accompanied by increased phosphorylation of AKT and its downstream targets GSK-3β and FOXO3a.(23) Notably, this percentage is higher than the frequency of PTEN aberrations (23% of the patients) and therefore has to be explained by the activation of AKT through other mechanisms. MYC may provide an alternative mechanism to activate AKT either directly or indirectly (e.g. MYC activates the expression of mir-17-92 and mir-19 that target PTEN mRNA)(63-65) (Figure 2C). Using an inducible MYC-dependent zebrafish T-ALL model, Gutierrez et al. found that established tumors regressed when MYC expression was turned off. This effect was circumvented by activating PI3K-AKT signaling, (66) showing that AKT activation is an important downstream effector of MYC that may drive GSI resistance. Moreover, the MYC gene is an important downstream target of NOTCH1, and T-ALL patients with activating mutations in NOTCH1 overexpress MYC.(67, 68) NOTCH binds a distal enhancer located far downstream of the MYC locus.(69, 70) This NOTCH-MYC enhancer region (N-Me) is duplicated in approximately 5% of T-ALL patients, acting as a “super-enhancer”.(69) In another 6% of adult and childhood T-ALL patients, MYC is ectopically activated due to a MYC translocation; importantly, these patients usually do not have NOTCH1-activating mutations.(71) MYC may also activate NOTCH1 via a positive feedback mechanism, as MYC suppresses the expression of miRNA-30, which targets the 3’ UTR of NOTCH1 (Figure 2C).(72) Accordingly, treatment of T- ALL xenografted mice with the bromodomain protein inhibitor JQ1 results in decreased MYC levels and also reverses MYC-induced resistance to GSI. (73, 74) Also, in human T-ALL cell lines, GSI-sensitive cells can be converted to GSI-resistant by the ectopic expression of MYC.(68, 75) Under normal conditions, MYC is phosphorylated by the kinase GSK-3β; phosphorylated MYC is then subjected to ubiquitination by FBXW7 and proteasome-mediated degradation (Figure 2C).(76, 77) Conversely, activated AKT can stabilize MYC protein by phosphorylating—and thereby inactivating—GSK-3β. Also, these findings may explain the observation that MYC and PTEN are reciprocally expressed in T-ALL.(78) Apart from enhancing cellular resistance to NOTCH1 inhibitors, MYC also enhances leukemia-initiating cell (LIC) activity and poor outcome in various mouse models of T-ALL. Mutant Fbxw7-R465C mice develop aggressive leukemias that acquire Notch1 mutations.(79) In

38 these mice, Myc levels are stabilized resulting in the expansion of leukemia cells that have enhanced self-renewal capacity and that express a stem cell-like expression profile.(79) The Tal1/Lmo2 transgenic mouse model develops spontaneous T-cell tumors that also acquire Notch1 mutations. Because Myc is a Notch target, Notch inhibition led to reduced LIC activity in these mice.(80) Reducing endogenous Myc levels led to increased survival and reduced numbers of leukemia cells with LIC potential in both models.(79, 81) Overall, these positive feedback loops between NOTCH, MYC, and AKT suggest that inhibitors of MYC or PI3K/AKT may help prevent resistance to NOTCH1-inhibiting therapies,(82) and also eliminate LIC activity in T-ALL. Co-targeting the PI3K pathway and MYC remarkably enhanced the elimination of LICs.(83) Another AKT activation mechanism is via the gene that encodes the IL7 receptor (IL7Ra), that also represents a direct target gene of NOTCH1.(84, 85) The IL7R gene is mutated in nearly 10% of T-ALL patients. These mutations cause the constitutive activation of STAT5 and AKT,(8, 86, 87) and can provoke GSI resistance (Figure 2D). For instance, expressing the IL7Ra can overcome the effects of NOTCH1 inhibition on the cell cycle and survival, thereby contributing to resistance.(84) Similar results were obtained by overexpressing IGF1R, which encodes insulin-like growth factor 1 receptor and is another NOTCH1 target (Figure 2D).(88) Also in these cases, NOTCH-inhibiting therapies may be more effective when combined with AKT inhibitors. Furthermore, enhanced AKT activity may limit leukemia sensitivity to steroid treatment,(89, 90) one of the cornerstone drugs in the treatment of human T-ALL. AKT was shown to directly phosphorylate (S134) and inactivate the steroid receptor NR3C1.(89) Combined steroid treatment with the dual PI3K-mTOR inhibitor BEZ235 (91) or the MK2206 AKT inhibitor (89) sensitized AKT-activated leukemic cells to steroid treatment.

Conclusion As a potent tumor suppressor, PTEN is considered to be the principal negative regulator of PI3K-AKT signaling. Inactivation of PTEN indirectly activates PI3K-AKT signaling, causing the uncontrolled proliferation of thymocytes, ultimately leading to T-ALL. Regardless of PTEN, AKT can be over-activated by a variety of signaling molecules, including PI3K, AKT, MYC, IL7R and IGF1R (Figure 2). Initial activation of AKT causes resistance to NOTCH1-inhibiting therapies. However, on the long-term, we suggest that AKT signaling may be dampened, thereby restoring responsiveness to NOTCH-inhibiting therapies. Overall, because AKT activation is central to a variety of leukemogenic mechanisms and crucial in the resistance to NOTCH1 inhibition, using

39 AKT inhibitors in current treatment protocols may be a promising strategy to treat NOTCH1- mutated T-ALL.

Acknowledgments RDM and KC-B were financed by the Children Cancer Free Foundation (Stichting Kinderen Kankervrij (KiKa 2008-29 and KiKa 2013-116). We thank EnglishEditingSolutions.com for editorial assistance.

Authorship Contribution: RDM, KC-B, RP, and JPPM wrote the manuscript. Conflict-of-interest disclosure: The authors declare no competing financial interests.

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44 70. Yashiro-Ohtani Y, Wang H, Zang C, Arnett KL, Bailis W, Ho Y, et al. Long-range enhancer activity determines Myc sensitivity to Notch inhibitors in T cell leukemia. Proc Natl Acad Sci U S A. 2014 Nov 18;111(46):E4946-53. 71. La Starza R, Borga C, Barba G, Pierini V, Schwab C, Matteucci C, et al. Genetic profile of T-cell acute lymphoblastic leukemias with MYC translocations. Blood. 2014 Dec 4;124(24):3577-82. 72. Ortega M, Bhatnagar H, Lin AP, Wang L, Aster JC, Sill H, et al. A microRNA-mediated regulatory loop modulates NOTCH and MYC oncogenic signals in B- and T-cell malignancies. Leukemia. 2015 Apr;29(4):968-76. 73. Delmore JE, Issa GC, Lemieux ME, Rahl PB, Shi J, Jacobs HM, et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell. 2011 Sep 16;146(6):904-17. 74. Knoechel B, Roderick JE, Williamson KE, Zhu J, Lohr JG, Cotton MJ, et al. An epigenetic mechanism of resistance to targeted therapy in T cell acute lymphoblastic leukemia. Nat Genet. 2014 Apr;46(4):364-70. 75. Sharma VM, Calvo JA, Draheim KM, Cunningham LA, Hermance N, Beverly L, et al. Notch1 contributes to mouse T-cell leukemia by directly inducing the expression of c-myc. Mol Cell Biol. 2006 Nov;26(21):8022-31. 76. Sears R, Nuckolls F, Haura E, Taya Y, Tamai K, Nevins JR. Multiple Ras-dependent phosphorylation pathways regulate Myc protein stability. Genes Dev. 2000 Oct 1;14(19):2501- 14. 77. Cross DA, Alessi DR, Cohen P, Andjelkovich M, Hemmings BA. Inhibition of glycogen synthase kinase-3 by insulin mediated by protein kinase B. Nature. 1995 Dec 21- 28;378(6559):785-9. 78. Bonnet M, Loosveld M, Montpellier B, Navarro JM, Quilichini B, Picard C, et al. Posttranscriptional deregulation of MYC via PTEN constitutes a major alternative pathway of MYC activation in T-cell acute lymphoblastic leukemia. Blood. 2011 Jun 16;117(24):6650-9. 79. King B, Trimarchi T, Reavie L, Xu L, Mullenders J, Ntziachristos P, et al. The ubiquitin ligase FBXW7 modulates leukemia-initiating cell activity by regulating MYC stability. Cell. 2013 Jun 20;153(7):1552-66. 80. Tatarek J, Cullion K, Ashworth T, Gerstein R, Aster JC, Kelliher MA. Notch1 inhibition targets the leukemia-initiating cells in a Tal1/Lmo2 mouse model of T-ALL. Blood. 2011 Aug 11;118(6):1579-90. 81. Roderick JE, Tesell J, Shultz LD, Brehm MA, Greiner DL, Harris MH, et al. c-Myc inhibition prevents leukemia initiation in mice and impairs the growth of relapsed and induction failure pediatric T-ALL cells. Blood. 2014 Feb 13;123(7):1040-50. 82. Roti G, Stegmaier K. New Approaches to Target T-ALL. Frontiers in oncology. 2014;4:170. 83. Schubbert S, Cardenas A, Chen H, Garcia C, Guo W, Bradner J, et al. Targeting the MYC and PI3K pathways eliminates leukemia-initiating cells in T-cell acute lymphoblastic leukemia. Cancer Res. 2014 Dec 1;74(23):7048-59. 84. Gonzalez-Garcia S, Garcia-Peydro M, Martin-Gayo E, Ballestar E, Esteller M, Bornstein R, et al. CSL-MAML-dependent Notch1 signaling controls T lineage-specific IL-7R{alpha} gene expression in early human thymopoiesis and leukemia. J Exp Med. 2009 Apr 13;206(4):779-91. 85. Ribeiro D, Melao A, Barata JT. IL-7R-mediated signaling in T-cell acute lymphoblastic leukemia. Adv Biol Regul. 2013 May;53(2):211-22. 86. Barata JT, Cardoso AA, Boussiotis VA. Interleukin-7 in T-cell acute lymphoblastic leukemia: an extrinsic factor supporting leukemogenesis? Leuk Lymphoma. 2005 Apr;46(4):483- 95. 87. Shochat C, Tal N, Bandapalli OR, Palmi C, Ganmore I, te Kronnie G, et al. Gain-of- function mutations in interleukin-7 receptor-alpha (IL7R) in childhood acute lymphoblastic leukemias. J Exp Med. 2011 May 9;208(5):901-8.

45 88. Medyouf H, Gusscott S, Wang H, Tseng JC, Wai C, Nemirovsky O, et al. High-level IGF1R expression is required for leukemia-initiating cell activity in T-ALL and is supported by Notch signaling. J Exp Med. 2011 Aug 29;208(9):1809-22. 89. Piovan E, Yu J, Tosello V, Herranz D, Ambesi-Impiombato A, Da Silva AC, et al. Direct reversal of glucocorticoid resistance by AKT inhibition in acute lymphoblastic leukemia. Cancer Cell. 2013 Dec 9;24(6):766-76. 90. Blackburn JS, Liu S, Wilder JL, Dobrinski KP, Lobbardi R, Moore FE, et al. Clonal evolution enhances leukemia-propagating cell frequency in T cell acute lymphoblastic leukemia through Akt/mTORC1 pathway activation. Cancer Cell. 2014 Mar 17;25(3):366-78. 91. Hall CP, Reynolds CP, Kang MH. Modulation of Glucocorticoid Resistance in Pediatric T- cell Acute Lymphoblastic Leukemia by Increasing BIM Expression with the PI3K/mTOR Inhibitor BEZ235. Clin Cancer Res. 2016 Feb 1;22(3):621-32.

46 Figure Legends

Figure 1. Schematic representation of the human PTEN gene located on chromosome 10q23. The PTEN gene contains nine exons, and the PTEN protein contains several functional domains, including a phosphatase domain (dark gray) and a C2 lipid-binding domain (light gray). The positions of nonsense insertion and deletion mutations are indicated by closed triangles, and missense mutations are indicates by open triangles. Microdeletions and deletions in the PTEN gene are shown below the exons. The number of patients with each mutation/deletion in our cohort of T-ALL patient samples is indicated.(21, 35)

Figure 2. Schematic overview of the upstream and downstream effectors of PTEN and associated molecular mechanisms that can activate AKT and lead to GSI resistance. (A) PTEN- PI3K-AKT mutations. (B) NOTCH mutations and AKT activation. (C) MYC signaling and AKT activation. (D) IL7R/IGF1R signaling and AKT activation. The molecules with activating and inactivating mutations are indicated in dark gray and light gray, respectively. Dashed lines/arrows represent processes that contribute to cellular GSI sensitivity, and that are frequently inactivated by inactivating mutations/rearrangements in PTEN or FBXW7. Solid lines/arrows represent GSI resistance mechanisms.

Figure 1

47 Figure 2

Chapter 3

PTEN microdeletions in T-cell acute lymphoblastic leukemia are caused by illegitimate RAG-mediated recombination events

Rui D. Mendes1,9, Leonor M. Sarmento2,9, Kirsten Canté-Barrett1, Linda Zuurbier1, Jessica G.C.A.M. Buijs-Gladdines1, Vanda Póvoa2, Willem K. Smits1, Miguel Abecasis3, J. Andres Yunes4, Edwin Sonneveld5, Martin A. Horstmann6,7, Rob Pieters1,8, João T. Barata2,10 and Jules P.P. Meijerink1,10

1Department of Pediatric Oncology/Hematology, Erasmus MC Rotterdam-Sophia Children’s Hospital, Rotterdam, the Netherlands; 2Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal; 3Cardiologia Pediatrica Medico Cirúrgica, Hospital Sta. Cruz, Lisboa, Portugal; 4Centro Infantil Boldrini, Campinas, SP, Brazil; 5Dutch Childhood Oncology Group (DCOG), the Hague, the Netherlands; 6German Cooperative Study Group for Childhood Acute Lymphoblastic Leukemia (COALL), Hamburg, Germany; 7Research Institute Children's Cancer Center Hamburg, Clinic of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 8Princess Maxima Center for Pediatric Oncology, Utrecht, Netherlands

9These authors are co-first authors; 10These authors contributed equally to this work

Blood, 2014, 124(4):567-78

Abstract PTEN inactivating mutations and/or deletions are an independent risk factor for relapse just like NOTCH-activating mutations or male gender for T-cell acute lymphoblastic leukemia (T-ALL) patients treated on DCOG or COALL protocols. Some monoallelic mutated or PTEN wild-type patients lack PTEN protein, implying that additional PTEN inactivation mechanisms exist. We show that PTEN is inactivated by small deletions affecting only a few exons in 8% of pediatric T- ALL patients. These microdeletions were clonal in 3% and sub-clonal in 5% of patients. Conserved deletion breakpoints are flanked by cryptic RAG-recombination signal sequences (cRSS) and frequently have non-template derived nucleotides inserted in between breakpoints, implying that microdeletions are the result of illegitimate RAG recombination activity. Identified cRSSs drive RAG-dependent recombination in a reporter system as efficiently as bona fide RSSs that flank gene segments of the T-cell receptor locus. Remarkably, equivalent microdeletions were also detected in thymocytes of healthy individuals. Similar to other PTEN aberrations, microdeletions strongly associate with the TALLMO-subtype characterized by TAL1 or LMO2 rearrangements. Primary and secondary xenotransplantation of TAL1-rearranged leukemia allowed development of leukemic subclones with newly acquired PTEN microdeletions. Ongoing RAG activity may therefore actively contribute to the acquisition of pre-leukemic hits, clonal diversification and disease progression.

50 Introduction

T-cell acute lymphoblastic leukemia (T-ALL) represents 10-15% of pediatric acute leukemias. Despite major therapeutic improvements due to treatment intensification and refined risk- adapted stratification during the past decade, ~30% of T-ALL cases relapse with very poor prognosis.1 T-cell transformation is characterized by aberrant expression of oncogenic transcription factors combined with inactivation of tumor suppressor genes (e.g. PTEN, CDKN2A) and/or activation of the NOTCH1 pathway.2 The ectopic expression of oncogenes is typically caused by chromosomal rearrangements—the so-called type A hits—that place oncogenes under the control of T-cell specific promoters or enhancer elements.3,4 The analysis of translocation breakpoints revealed frequent involvement of illegitimate V(D)J recombination in these translocations by binding of recombination activating gene (RAG)-1/2 proteins to sequences that resemble authentic recombination signal sequences (RSS).5 These recurrent chromosomal rearrangements activate several oncogenes, such as TAL1, LMO2, TLX3, TLX1 or NKX2- 1/NKX2-2, which are believed to represent the clonal disease drivers.2,6 Besides near mutually exclusive type A mutations, recurrent genetic aberrations that affect cell viability and/or proliferation—the so-called type B hits—are found in nearly all T-ALL genetic subgroups. Type B mutations include NOTCH1-activating mutations affecting NOTCH1 and FBXW7 that are found in over 60% of pediatric T-ALL patients 7-11 (reviewed in Ferrando 12), as well as less frequent events such as IL7R mutations in around 10% of T-ALL cases.13,14 In addition, mutations in the phosphatase and tensin homolog (PTEN) tumor suppressor gene have been associated with poor prognosis,15-18 resulting in overactive PI3K–AKT signaling that drives enhanced cell proliferation and cell metabolism, and impairs apoptosis.16,19,20 PTEN is considered to be a haploinsufficient tumor suppressor gene, because PTEN dose determines cancer susceptibility.21-23 The majority of PTEN aberrations in T-ALL are deletions affecting the entire PTEN locus or mutations that truncate the membrane binding C2-domain.15,18 In our previous studies, we detected PTEN aberrations in 13-20% of T-ALL patients18,24 and revealed that those mutations are especially associated with TAL or LMO rearrangements and nearly absent in TLX3-rearranged T-ALL.18 In general, PTEN mutated T-ALL appears to be devoid of NOTCH1-activating mutations.18 Interestingly, we did not observe differential AKT activation when comparing PTEN mutant/deleted with wild-type patient samples, indicating that other mechanisms may influence the PI3K-AKT pathway. In this respect, non-deletional posttranslational inactivation of PTEN,24 rare mutations in PIK3CA (encoding PI3K) and AKT

51 themselves16, or PI3K-AKT pathway activation downstream of activated NOTCH1 have been described.15 However, none of these mechanisms explain the absence of PTEN protein in some T-ALL patient samples that have retained at least one PTEN wild-type allele.18 In this study, we have used multiplex ligation-dependent probe amplification (MLPA) to investigate copy-number variations among PTEN exons to detect potential additional PTEN deletions. We identified PTEN microdeletions in T-ALL patient samples and we provide evidence that these are driven by illegitimate RAG-mediated recombination events.

Materials and Methods

Patients A total of 146 primary pediatric T-ALL patient samples were enrolled in the Dutch Childhood Oncology Group (DCOG) protocols (n=72)25-27 or the German Co-Operative Study Group for Childhood Acute Lymphoblastic Leukemia study (COALL-97) (n=74) were included in this study.28 Normal thymocytes were isolated from thymic tissue obtained from children undergoing cardiac surgery.24,30 Informed consents were in accordance with the Institutional Review Boards of the ErasmusMC (Rotterdam, the Netherlands), the Ethics Committee of the City of Hamburg, Germany, the Hospital Sta. Cruz, Centro Hospitalar de Lisboa Ocidental (Lisboa, Portugal) and the Declaration of Helsinki.

Statistics

52 Statistics was performed using IBM SPSS Statistics 21 software (IBM, Armonk, NY, USA). The Pearson’s Chi-square was used for nominal distributed data, the Fisher’s exact test was alternatively used in case the number of patients in individual groups was lower than five. The Mann-Whitney-U test was used for continuously distributed data. Differences in relapse-free survival were tested using the log-rank test. Proportional risk for relapse was done by univariate and multivariate Cox regression analyses. The recombination efficiencies of cRSSs were compared using a one-way ANOVA with the Bonferroni´s multiple comparison post-test. Data were considered significant when p≤0.05 (two-sided).

See Supplementary Materials and Methods for further experimental details.

Results

PTEN microdeletions in T-ALL patients In our previous study, we identified various T-ALL primary patient samples that lack PTEN protein expression and seemed PTEN wild-type or that contained an inactivating mutation or PTEN deletion in only one allele (summarized in Table 1).18 To identify additional PTEN inactivating mechanisms, we performed MLPA analysis to screen for potential microdeletions affecting single or a few PTEN exons that had been missed by array-CGH and FISH analyses. We analyzed 146 T-ALL patient samples for copy-number alterations in any of all 9 coding exons. Heterozygous microdeletions were detected in 3 T-ALL patients (Figure 1A), encompassing exons 2 and 3 in 2 patients (#21, #11) and exons 4 and 5 in another patient (#20). Accordingly, 2 out of the 3 patients (#11, #20) demonstrated defective PTEN-splicing with previously unknown underlying genetic aberrancies.18 A fourth patient (#12) was identified with a homozygous deletion of exons 2 and 3 that was confirmed by high-resolution array-CGH analysis (Figure 1A,B). In order to clone the breakpoint regions of these microdeletions, a PCR-based strategy was designed for introns 1 and 3 (patients #21, #11 and #12) and intron 3 and 5 (patient #20) (Figure 2A,B), and resulting positive reactions were cloned and sequenced. These analyses predicted microdeletions of ~65Kb that encompassed exons 2-3 and of ~11Kb that encompassed exons 4-5 (Figure 2B). The homozygously deleted patient (#12) revealed different breakpoints that point to independent deletion events for each allele, with insertion of random bases in between breakpoints for one allele (Figure 2B). Breakpoints for the exon 2-3

53 microdeletion in patient #21 were identical to breakpoints of one allele of patient #12 and also lacked insertion of random bases. T-ALL patient #11 had a similar exon 2-3 deletion that shared the identical breakpoint in intron 3 but had an alternate breakpoint in intron 1 (Figure 2A-C). Breakpoints for the fourth T-ALL patient #20 with a microdeletion that affected exons 4 and 5 were located in introns 3 and 5 (Figure 2B). All three types of microdeletions result in out-of- frame PTEN transcripts (Figure 2C). As MLPA does not allow for sensitive detection of subclones with microdeletions, we performed PCR analysis to screen the T-ALL cohort for similar microdeletions. Seven additional patients were identified with deletions affecting exons 2-3 that were similar to the breakpoints as observed in patients #21 and #12 (Figure 2B). Based on the conservation of breakpoints, this microdeletion was denoted as a type I microdeletion. One additional patient was identified with breakpoints similar to patient #11, therefore this deletion was denoted as a type II microdeletion. The deletion affecting exons 4 and 5 as identified in patient #20 was accordingly denoted as a type III microdeletion. These deletions had not been detected before by array-CGH, FISH or MLPA analyses. One patient sample (#20) had already been identified by MLPA as having a clonal microdeletion affecting exons 4 and 5, but also contained a subclonal type I microdeletion in exons 2 and 3 as detected by PCR (Figure 2B and Table 1). In another case (#19), array- CGH had revealed a heterozygous PTEN deletion of exons 3-9, but now PCR also revealed a subclonal type I exon 2-3 microdeletion (Table 1). Sequencing of the breakpoints in these additional T-ALL cases revealed that five out of the seven type I deletions and the type II deletion involved the insertion of unique, random nucleotide sequences, thereby excluding false positives due to PCR contamination. Notably, the PCR product for these patient samples as visualized by gel electrophoresis was much weaker than those for the 4 patient samples with clonal microdeletions (data not shown). This strongly indicates that these deletions must be present on the subclonal level and therefore only detectable by specific PCRs. Overall, we have identified PTEN microdeletions in 11 out of 146 T-ALL patients (8%), comprising a total of 13 deletional events. Only 4 patients presented these mutations at the clonal level.

Microdeletion breakpoints are flanked by cryptic RAG recombination signal sequences The conservation of breakpoints among patient samples and the inclusion of non-template derived nucleotides by terminal deoxynucleotidyltransferase (TdT)31 in most breakpoint regions pointed to a RAG-mediated deletion mechanism. We then searched for the presence of cryptic RAG recombination signal sequences (cRSS) that could function as putative RAG-mediated recombination signals, such as the RSS involved in T- or B-cell receptor gene segment

54 rearrangements.32 Analysis of sequences directly flanking the breakpoints immediately revealed typical CAC canonic trinucleotides (Figure 2B and Table S5), which is a hallmark of the heptamer sequences of RSSs. The search for nearby nonamer sequences with A-nucleotide enrichment revealed a putative 12-spacer cRSS in intron 3 with a 5' to 3' orientation (cRSS1). A 23-spacer RSS was identified that directly flanks the breakpoint in intron 5 (cRSS2), and two others were identified flanking both breakpoints in intron 1 (cRSS3 and cRSS4; Figure 2A,B and Table S5). All 23-spacer cRSSs (cRSS2, cRSS3 and cRSS4) are present in a 3' to 5' orientation with respect to the PTEN reading frame orientation, and are therefore correctly positioned to allow illegitimate RAG-mediated recombinations with cRSS1 (Figure 2A,C). In this scenario, RAG1/2 molecules bind a pair of 12- and 23-RSSs resulting in two DNA double-strand breaks adjacent to each heptamer. Most microdeletion breakpoints are the consequence of heptamer- to-heptamer sequence fusions resembling signal joints of excision circles that are generated during normal T- or B-cell receptor gene segment rearrangements (Figure 2D, top): Types I and II microdeletions result from cleaved DNA sequences 3-prime of cRSS3 or cRSS4, respectively, that are fused to sequences 5-prime of cRSS1. This retains both cRSSs in the genomic sequences that flank the deletion breakpoints as depicted in Figure 2D. The type III deletion resembles a typical coding joint that results from cleaved DNA sequences 5-prime of cRSS1 that are fused to sequences 3-prime of cRSS2 resulting in the loss of cRSSs from the genomic sequence (Figure 2D, bottom). T- or B-cell receptor coding joints give rise to fused gene segments with potential exonuclease processing of both ends and incorporation of random nucleotides whereby directly flanking RSSs and intervening DNA sequences are lost as excision circles. For the type III deletion of patient #20 (Figure 2B), this led to the fusion of sequences 5- prime of cRSS1 to sequences of 3-prime of cRSS2 with loss of 14 nucleotides and incorporation of 17 GC-rich N-nucleotides.

Prediction of cRSSs To further characterize these cRSSs and estimate their recombination potential, we calculated RSS Information Content (RIC) scores.29 Cryptic RSSs with RIC scores close to RIC score threshold levels that discriminate bona-fide functional RSSs flanking gene segments of antigen receptor loci from cRSSs (i.e. -38.81 for 12-spacer RSSs and -58.45 for 23-RSSs) were further investigated.29 This search in the PTEN locus predicted a 12-spacer cRSS1 with a strong RIC score of -34.23 as well as 23-spacer cRSS2 (-55.59) and cRSS3 (-59.78) with RIC scores that were close to the threshold levels separating RSSs from cRSSs. A 23-spacer cRSS4 was predicted with a RIC score of -75.59 that is barely above the mean background RIC score value

55 for 39-nucleotide non-RSS DNA sequences (-77.76). Thus, the obtained RIC scores for cRSS1, cRSS2 and cRSS3 strongly support PTEN microdeletions as RAG-mediated recombination events with similar recombination potential to that of bona-fide RSSs flanking immunoglobulin V(D)J gene segments.

Cryptic RSS1-4 support RAG-mediated recombination We then tested whether the predicted cRSS1-4 could functionally mediate RAG recombinations. We used the GFPi-mRFP RAG reporter construct30 (Figure 3A), in which the 12-spacer cRSS1 was cloned in combination with a consensus 23-spacer RSS. Also, the 23-spacer RSSs (cRRS2, cRSS3 and cRRS4) were cloned in combination with a consensus 12-spacer RSS. Recombination efficiency of each variant GFPi-cRSS-mRFP construct was measured by flow cytometry as the frequency of GFP-positive (recombination-positive) HEK293T cells within the population of RFP-positive (transfected) cells (Figure 3A). Indeed, all four PTEN cRSSs were able to mediate RAG-dependent recombination of the GFPi substrate (Figure 3B). Recombination efficiencies were 7.5±0.19% for cRSS1 (Figure 2B, left panel), 2.2±0.15% for cRSS2, 4.1±0.21% for cRSS3 and 2.1±0.16% for cRSS4 (right panel). For comparison, the putative 12-spacer cRSS SCL(12)30 or the 23-spacer cRSS SCL(23)33 from the human SCL gene yielded 1.3±0.09% and 1.1±0.13% of GFP-positive cells, respectively. Both of these SCL cRSSs were used as references for the lower limit of detection in the GFPi-mRFP RAG reporter assay, as these do not give rise to distinct GFP-positive cell populations in the reporter assay. In contrast, the 12-spacer RSS that flanks the Jβ2-2 gene segment of the mouse TCRβ locus yielded 8.0±0.31% of GFP-positive cells. Also, the 12-spacer cRSS that is involved in recurrent LMO2 translocations in T-ALL5 yielded 11.4±0.30% of GFP-positive cells. These reporters highlight the capability of the recombination assay to measure low-efficiency RSS and cRSS activities. Despite the low frequencies of recombination, cRSS2-4 reporters give rise to distinct populations of GFP-positive cells (Figure 3B), in contrast to SCL(12) and SCL(23) cRSSs. Moreover, the efficiencies of recombination of PTEN cRSS1-4 differed significantly from the those of SCL(12) or SCL(23) cRSSs (Figure 3C). These results strongly support the involvement of predicted cRSS1-4 in PTEN microdeletions in illegitimate RAG-mediated recombination events. Additionally, the recombination potential of these cRSSs are in line with the observed frequencies of type I microdeletions (cRSS3-cRSS1) versus type II (cRSS4-cRSS1) and type III (cRSS1-cRSS2) microdeletions in T-ALL patients (Figure 2B).

PTEN microdeletions in xenografted human T-ALL cells

56 Subclonal microdeletions in PTEN, even in patients that already had undergone clonal inactivating events affecting one allele, strongly imply that acquisition of microdeletions is an ongoing phenomenon in T-ALL leading to clonal diversity.34 To test this, we performed primary and secondary xenotransplantation experiments into NSG mice (Figure 4A) using TAL1- rearranged T-ALL blasts from patient (#24) at diagnosis that had a subclonal microdeletion (Figure 4B). Several months post-transplantation, mice developed overt leukemia. Primary (X1) and secondary (X2) xenotransplanted material was then analyzed for the presence of PTEN microdeletions in bone marrow, thymus, spleen and liver biopsies. Using MLPA analysis, no PTEN microdeletions were detected (data not shown), indicating that the subclonal PTEN microdeletion in the diagnostic patient material had not been clonally selected following xenotransplantation. Three distinct, subclonal PTEN microdeletions were detected by PCR in thymocyte and liver biopsies: One (X1-24 thymus-1) was identical to the microdeletion as originally identified in this patient (Figure 4B), whereas 2 novel microdeletions were detected suggesting that these had occurred upon serial retransplantation.

PTEN deletions are associated with TALLMO T-ALL patients PTEN aberrations have been associated with a low incidence of NOTCH1-activating mutations, but with a high incidence of rearrangements in TAL1- and/or LMO2-related oncogenes.18 We now extend these findings, totaling 26 out of 146 T-ALL patients (18%) that have PTEN aberrations including point, missense or nonsense mutations, entire locus deletions and/or microdeletions at the clonal or subclonal level as summarized in Table 1. Twelve patients had clonally inactivated PTEN on both alleles and 10 patients on one allele. Evidence for subclonal PTEN aberrations was found in 11 patients, seven of whom also had clonally inactivated PTEN at least in one allele. The other four patients had either a subclonal missense mutation (patient #23) or subclonal microdeletions (3 patients) only. Still, for 8 T-ALL patients for whom protein data were available, absence of PTEN protein could not be solely explained by the genetic aberrations found, suggesting that additional PTEN inactivating mechanisms await identification. Overall, our previously observed association with TAL or LMO-rearranged leukemia18 became considerably more significant (p=0.003; Table S6). Also, the significance levels for absence of these mutations in TLX3-rearranged T-ALL (p=0.002; Table S6) and reduced overlap with NOTCH1-activating mutations were further strengthened (p=0.001, Table S6).

PTEN aberrations and outcome

57 Our results do not support observations by others34 that PTEN-inactivated T-ALL subclones become selected during disease progression giving rise to relapse. Therefore, we regarded T- ALL patients with subclonal PTEN aberrations as wild type patients in outcome analyses. PTEN/AKT aberrant T-ALL patients, including patients lacking PTEN protein expression, were not significantly associated with poor outcome in both treatment cohorts (5yrs RFS for PTEN/AKT mutant patients is 64±15% versus 70±6% for wild type patients on DCOG protocols and 57±15% versus 76±6% for patients on COALL protocols). This is due to the fact that PTEN/AKT mutations and NOTCH-activating mutations predominantly behave as mutually exclusive mutations. In addition, NOTCH-activating mutations have a strong trend towards poor outcome (5yrs RFS for NOTCH-activated patients is 62±8% versus 82±8% for wild type patients on DCOG protocols and 68±8% versus 80±9% for patients on COALL protocols, (p=0.06 (stratified for protocol); and supplementary Table S7).35 However, if NOTCH-activated and PTEN/AKT mutated T-ALL patients are being compared to wild type patients, wild type patients demonstrate significantly fewer relapses (stratified p=0.04; Figure 5), albeit having more frequent events including toxic deaths and secondary malignancies.18 Using the Cox regression proportional hazard method, NOTCH-activating and PTEN/AKT mutations were investigated along with male gender and the presence of TLX3 rearrangements that both negatively relate with poor outcome (Supplementary Table S7 and Table 2). NOTCH1-activating mutations and PTEN/AKT mutations did not significantly predict for increased risk for relapse in univariate analyses, but both were identified as strong, independent risk factors along with male gender in multivariate analysis (Table 2).

PTEN microdeletions in healthy human thymocytes The presence of recombination-prone cRSSs in PTEN intron sequences led us to speculate that microdeletions may occur in healthy thymocytes. Screening DNAs that were isolated from thymocytes of non-leukemic children for the presence of PTEN microdeletions by PCR revealed evidence for subclonal type I deletions in 3 out of 11 (27%) thymocyte biopsies. Two of those microdeletions had unique random nucleotide sequences inserted in between the breakpoints (Figure 4C), ruling out false PCR positivity due to contamination. Thus, RAG-mediated PTEN microdeletions are not exclusive to T-ALL, but also occur during normal T-cell development.

58 Discussion

PTEN has been identified as a haploinsufficient tumor suppressor gene,21-23 for which gene mutations and/or deletions have been associated with poor outcome in T-ALL in various17,18,36,37 but not all studies.16,38 For T-ALL patients treated on DCOG ALL-7/8/9 or COALL97/03 treatment protocols, we demonstrate that clonal PTEN inactivating aberrations or loss of PTEN protein is an independent factor that predicts for relapse just like NOTCH-activating mutations and male gender. About half of the T-ALL patients that retained one wild type allele do not express PTEN protein. This indicates that additional genetic, epigenetic or post-translational inactivating events are expected. We have identified recurrent inactivating PTEN microdeletions in T-ALL patients due to illegitimate RAG-mediated recombination events, mediated through cryptic RSSs (cRSSs). Taking into account all point, missense or nonsense mutations as well as deletions including microdeletions, 18% of T-ALL patients in our patient cohort harbor PTEN inactivating aberrations. Increasing evidence suggests that cRSSs can participate in oncogenic mechanisms, including chromosomal translocations in lymphoma, B-ALL39,40 and T-ALL4,41,42, such as SIL- TAL1 gene fusions and HPRT deletions. Different chromosomal translocation mechanisms have been described as a consequence of erroneous rearrangements between cRSS that flank oncogenes with RSS-sequences of T-cell receptor gene segments. Alternatively, broken DNA strands near oncogenes become mistakenly fused through a non-cRSS mechanism to T-cell receptor gene during V(D)J-assembly (reviewed in Radich and Sala43). Other illegitimate, cRSS- driven coding-joint recombination events may cause intrachromosomal deletions such as described for IKZF1,44,45 ERG1,46 BTG1,47 and CDKN2A/B48-50 in humans and Notch151,52 and Bcl11b53 in mice. PTEN microdeletions occur as a consequence of aberrant RAG-mediated recombination events, and breakpoints are flanked by cRSSs containing heptamer and nonamer sequences separated by 12 or 23 nucleotide spacers. These cRSSs facilitate recombinations in in vitro recombination assays54 with efficiencies that match the frequencies of different types of microdeletions in T-ALL patients. Approximately, one third of all signal joint-related type I and II microdeletions have perfect heptamer-to-heptamer fusions that lack incorporation of random nucleotides just like IgH or TCR excision circle signal-joints. Two third of microdeletion signal junctions represent atypical joints that incorporated GC-rich N-nucleotides, phenomena which is also observed in V(D)J-associated signal junctions in mouse lymphocytes.55 The T-cell receptor- mediated translocation in T-ALL line SUP-T1 is a comparable atypical signal junction.56 Some of these microdeletion atypical signal joints (patient #12, #26 and #11) had undergone exonuclease

59 processing of signal ends (Figure 2B). Non-canonic heptamer sequence variations may destabilize the RAG complex, allowing alternative joining mechanisms of coding-ends and signal-ends.57 This may include open-and-shut joint recombinations,58,59 resulting from a single RAG cleavage adjacent to cRSS heptamers, exonuclease processing and insertion of random nucleotides before re-ligation of the DNA ends. This may explain one rare T-ALL case24 with a mutation that replaces 13 nucleotides including the start codon by 15 random nucleotides. This mutation is flanked by a 12-spacer cRSS with a strong RIC score of -45.48 that allows RAG- mediated recombinations equal to the efficiency (2.5±0.13%) of the mouse IgH locus VH/87 RSS (L.M.S., unpublished data and Davila et al60). However, no other T-ALL patient in our current series had an equivalent mutation at this position, indicating that these events are rare. The identification of subclonal PTEN microdeletions—as well as entire PTEN locus deletions18—indicates that RAG activity may be ongoing in (at least part of) the leukemic cell population. This may explain clonal diversity and selection that results in disease progression and relapse. This latter is also supported by in-vitro recombination assays using T-ALL cell lines, and demonstrate that about one percent of leukemic cells or less will undergo recombinations of the reporter construct within a one week time-frame.30 Since intraclonal heterogeneity at diagnosis and clonal evolution at relapse are known to occur in ALL,46,61-64 we checked whether PTEN microdeletions in minor leukemic clones at presentation of disease become clonally selected following xenograft transplantation just like lentiviral PTEN-silenced T-ALL blasts.34 However, we did not observe preferential selection of leukemic cells with PTEN microdeletions to near clonal levels following xenotransplantation. This could be explained by preferential outgrowth of leukemic subclones having other mutations that were advantageous for engraftment in mice over subclones having PTEN microdeletions. Furthermore, additional and new illegitimate RAG-mediated PTEN microdeletions possibly as consequence of ongoing RAG activity were detected that were not found in primary leukemic cells. We cannot formally rule out that subclonal selection of a leukemic subpopulation with a novel PTEN microdeletion occurred from a PCR-undetectable subclone that was already present at diagnosis. In addition, one patient had a subclonal missense mutation, indicating that there is an ongoing pressure on TALLMO-dysregulated leukemic cells to inactivate remaining wild-type PTEN alleles. RAG activity also results in PTEN microdeletions in developing thymocytes of healthy individuals. These rearrangements may facilitate a pre-malignant condition from which leukemia can develop. Likewise, Marlunescu et al5 described two mechanisms of illegitimate V(D)J chromosomal rearrangement that were found in healthy children, i.e. the Dδ2/LMO2

60 recombination in the t(11;14)(p13;q11) and the TAL2/TCRβ translocation t(7;9)(q34;q32),65 known as driving oncogenic lesions in T-ALL. Overall, our discovery of PTEN microdeletions has reinforced the fact that PTEN aberrations are especially abundant in TAL- or LMO-rearranged leukemia but not in TLX3- rearranged patients,18 as also observed in adult T-ALL patient series37. PTEN abnormalities seem to be associated with a reduced incidence of NOTCH1-activating mutations. The TALLMO subtype represents an immunophenotypically mature subtype of arrested leukemic cells in T- ALL, in which ongoing RAG-activity creates an opportunistic and extended time window for cRSS-mediated illegitimate recombination events. These may provoke disease progression and relapse in leukemia patients, adding a new level of complexity that should be addressed in the development of future antileukemic strategies for ALL. Taking into account all currently known PTEN inactivation mechanisms —PTEN mutations, entire locus deletions and PTEN microdeletions—, some seemingly wild type T-ALL patients still lack PTEN protein expression indicating that other PTEN inactivation mechanisms await identification.

Acknowledgements The authors would like to thank Gustavo G.L. Costa and Izabella A.P. Neshich for help with computational detection of cRSSs. RM, KCB and LZ were financed by the Stichting Kinderen Kankervrij (Grant no. KiKa 2007-12, KiKa 2008-29 and KiKa 2013-116). LMS received a postdoctoral fellowship from Fundação para a Ciência e a Tecnologia (FTC). This work was supported by grants PTDC/SAU-ONC/113202/2009 from FCT (to JTB), and FAPESP 08/10034- 1.

Authorship Contribution: R.D.M., L.M.S., J.T.B. and J.P.P.M. designed the study and wrote the manuscript; R.D.M., W.K.S., L.Z. and J.G.C.A.M.B.-G. performed the MLPA analyses, breakpoint mapping and PCR-based screening of PTEN microdeletions; J.A.Y. performed the computational detection of putative RAG recombination signal sequences; L.M.S. and V.P. were responsible for generation of GFPi-PTEN cRSS reporter constructs and recombination assays; K.C.-B. and W.K.S. performed the xenotransplantation experiments; J.P.P.M. performed the statistical analyses of the data; M.A., E.S., M.H. collected and provided patient samples and their characteristics; R.D.M., L.M.S., K.C.-B., R.P., J.T.B., J.P.P.M. analyzed and interpreted data; and all authors read, revised, and approved the paper. Conflict-of-interest disclosure: The authors declare no competing financial interests.

61 References

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65 Legends of Figures

Figure 1. Identification of PTEN microdeletions in T-ALL patients. (A) MLPA electropherograms of normal reference DNA and representative examples of T-ALL patients with heterozygous or homozygous PTEN microdeletions affecting exons 2-3 or a heterozygous deletion of exons 4-5. Fluorescence intensities of amplified PCR products for specific PTEN exons are shown. PCR product sizes are shown at the top. Each arrow points to a homo- or heterozygously deleted exon. (B) Array-CGH plot exhibiting the homozygous PTEN exon 2-3 microdeletion in one T-ALL patient sample.

Figure 2. Breakpoints of PTEN microdeletions. (A) Schematic representation of the PTEN gene. Missense mutations are represented by open triangles above the exons, whereas a silent mutation is presented as a filled grey triangle as shown before18. Nonsense insertion/deletion mutations are indicated by a filled black triangle. Left or right-pointing open triangles in introns 1, 3 and 5 represent cRSSs. (B) Sequences of cloned intron 1-3 type I and type II breakpoints and the intron 3-5 type III breakpoint for T-ALL patients with PTEN microdeletions. Cryptic recombination signal sequences (cRSS) are indicated by a box with the canonic CAC trinucleotide sequences or the corresponding GTG nucleotides in heptamer sequences indicated in bold and underlined. Insertion of non-template, random nucleotides are shown in bold. (C) Examples of sequence traces of cDNA resulting from type I, II and IIImicrodeletions. (D) Involvement of specific cRSSs in illegitimate RAG-mediated recombination events resulting in types I and II microdeletions (signal joint) and aberrant PTEN splice variant or the type III with the aberrant exon 4-5 microdeletion PTEN transcript (coding joint).

Figure 3. Intronic PTEN cryptic RSS mediate RAG recombination events. (A) Upper panel: Linear representation of the GFPi reporter construct that results in the inversion of GFP coding sequence during RAG-mediated recombination, and consequent GFP expression. The inverted GFP sequence (light green box) is flanked by a proximal 12-spacer RSS (light grey triangle) and a distal 23-spacer RSS (dark grey triangle) followed by the IRES-RFP as transfection control reporter (red box). GFP positivity is a measure for recombination potential. Lower panel: Control in vitro RAG recombination assay; flow cytometry analysis of HEK293T cells transiently transfected with either an irrelevant, mock vector (in the absence of RAG1/2 expression vectors; negative control) or the GFPi-reporter construct containing the consensus 12- and 23-RSS in the presence of RAG1/2 expression vectors.30 The flow cytometry plots show the expression of GFP and RFP within gated live cells defined by FSC

66 and SSC parameters (not shown) and the values represent the percentage of each cell population in the quadrants. The gate used to discriminate RFP-positive from RFP-negative cells is depicted by a red square and used for the contour plot analysis. The efficiency of recombination is indicated as the percentage of GFP-positive (recombination positive) cells within the RFP-positive (transfected) population. (B) Flow cytometry analysis of HEK293T cells transiently transfected with the GFPi variant constructs containing specific 12-spacer cRSS (LMO2, SCL/TAL1, PTEN-cRRS1 or the Jβ2.2-RSS) site combined with the consensus 23-spacer RSS (left panel). The GFPi variant constructs containing the consensus 12-RSS30 was combined with 23-spacer PTEN cRSS2, cRSS3, cRSS4 or the control SCL/TAL1 23-spacer cRSS version (right panel). The human LMO2 12-spacer cRSS and the mouse Jβ2-2 bona fide RSS were used to establish the range of recombination activitites for low-efficiency RSSs as measured by the GFPi reporter assay. The 12- and 23- spacer versions of the human SCL/TAL1 cRSS were used to define the lower limit of detection of cRSS function in this reporter assay. Average percentage ±SD of GFP+ cells in the RFP+ population are derived from 4-5 independent experiments. (C) Recombination index was determined by normalizing the recombination efficiencies of each indicated reporter to that of GFPi Con12/23 and recombination efficiencies were calculated subtracting the GFP background of each respective unrecombined control. Values represent the mean±SEM of 3 independent experiments with 3 replicates per condition; * p < 0.05; ** p < 0.01 and *** p < 0.0001.

Figure 4. PTEN microdeletions in xenotransplants of a T-ALL primary patient sample and in human thymocytes from healthy individuals. (A) Schematic representation of the xenotransplantation strategy. Several months post-transplant of the patient’s (#24) leukemic cells into immunodeficient NSG mice (n=9), we collected cells from bone marrow (BM), thymus (Thy), spleen (Spl) and liver. Primary (X1) and secondary (X2) xenotransplanted material was then analyzed for the presence of any of the three different PTEN microdeletions. (B) Breakpoint sequences of PTEN microdeletions as detected in samples from primary (X1) and secondary (X2) xenotransplanted mice. Canonic CAC trinucleotide sequences or the corresponding GTG nucleotides in heptamer sequences are indicated in bold and underlined. (C) Sequences of the breakpoints for PTEN type I microdeletions as identified in thymocytes of healthy individuals (H-Thy1 – H-Thy3).

Figure 5. T-ALL patients lacking PTEN/AKT mutations and NOTCH-activating mutations have a good outcome. Relapse free survival curves (RFS) for T-ALL patients treated on (A)

67 Dutch DCOG ALL7/8 or 9 protocols or (B) German COALL-97/03 protocols. Green line; NOTCH-activating mutations including mutations in NOTCH and FBXW7, red line; PTEN- inactivating or AKT-activating mutations, blue line; NOTCH-activating mutations and PTEN- inactivating or AKT-activating mutations combined, black line; wild type for NOTCH/FBXW7 and PTEN/AKT. Tick-marks in figures refer to patients that are lost from further follow-up. The numbers of patients included at various time points in these studies have been shown.

68 Table 1. PTEN genetics of T-ALL patients

Patient PTEN mutation PTEN Deletion PTEN Deletion Genomic Aberrant PTEN Cytogenetic NOTCH/FBXW7 Allele A Allele B (FISH/Array-CGH) (MLPA) breakpoint PCR transcript protein aberration mutation

Clonal inactivation of 2 alleles

1 R129G T231fsX24 - - ND Mutant LMO3 - 2 F144fsX37 R232fsX23 Subcl- Del - - ND Absent LMO2 PEST 3 P246fsX11 - Het Del Het Del exons 2-9 - ND Absent SIL-TAL1 - 4 D235fsX9 P245fsX12 ND ND - ND Absent CALM-AF10 PEST 5 R232fsX13 Q244fsX8 - - - ND Absent Unknown - 6 R233fsX10 P245fsX9 - - - ND ND Unknown FBXW7 7 R232fsX10 P243fsX18 - - - ND ND TLX1 - 8 R232fsX10 P245fsX14 - - - ND Absent LMO2 FBXW7 9 L180fsX2 I305fsX7 - - Intron 1-3 Del (type II; Subcl) ND Absent NKX2-5 HD 10 C104fsX2 K236fsX5 - - - ND Absent SIL-TAL1 - 11 P245fsX3 - ND Het Del exons 2-3 Intron 1-3 Del (type II, Clonal) in1/2-ex4 ND MYC - 12 - - Hom Del/FISH negative Hom Del exons 2-3 2 Intron 1-3 Del variants (type I, Clonal) in1/2-ex4 Absent SIL-TAL1 -

Clonal inactivation of 1 allele

13 R129fsX4/P245fsX3 - Subcl Del Het Del exons 1-9 - ND ND Unknown - 14 R232(STOP) - - - - - ND SIL-TAL1 - 15 C249fsX10 - - - Intron 1-3 Del (type I; Subcl) - Absent Unknown - 16 T231fsX14 - - - - - Absent NKX2-1 HD/FBXW7 17 T276A - - - Intron 1-3 Del (type I; Subcl) - Absent Unknown - 18 - - Het Del Het Del exons 1-9 - WT Absent Unknown PEST 19 - - Het Del Het Del exons 3-9 Intron 1-3 Del (type I; Subcl) Altered Absent SIL-TAL1 - 20 - - - Het Del exons 4-5 Intron 3-5 Del (type III, Clonal) / Intron 1-3 Del (type I; Subcl) ex3-ex6 splice Absent SIL-TAL1 - 21 - - ND Het Del exons 1-3 Intron 1-3 Del (type I, Clonal) in1/2-ex4 ND SIL-TAL1 HD 22 - - Het Del Het Del exons 2-9 - ND ND Unknown -

Subclonal inactivating event only

23 R232fsX (Subcl) - - - - - ND SIL-TAL1 - 24 - - - - Intron 1-3 Del (type I; Subcl) ND ND SIL-TAL1 HD/PEST 25 - - ND - Intron 1-3 Del (type I; Subcl) ND ND SIL-TAL1 - 26 - - - - Intron 1-3 Del (type I; Subcl) ND ND Unknown - Others

27 ------Absent TAL2 FBXW7 28 ------Absent TAL1 HD PTEN frameshift mutations are indicated with the number of encoded amino acids in the alternative reading frame. PTEN deletion status was determined by fluorescent in situ hybridization (FISH), array-comparative genomic hybridization (array-CGH) and/or multiplex ligation-dependent probe amplification (MLPA). Introns harboring the genomic breakpoints of PTEN deletions and/or microdeletions have been indicated. Exons for alternative spliced PTEN transcripts are indicated. Abbreviations: Del, deletion; FS, frameshift; HD, NOTCH1 heterodimerization domain mutation; Het, heterozygous; Hom, homozygous; PEST, NOTCH1 mutation in the proline, glutamine, serine and threonine-rich C-terminal region; Subcl, subclonal.

69 Table 2. NOTCH1-activating and PTEN/AKT mutations predict for poor outcome in pediatric T-ALL treated on DCOG ALL7/8/9 or COALL-97/03 protocols. Univariate analyses using Cox regression model n Hazard ratio 95% confidence interval P Male gender 146 3.278 1.267-8.486 0.014 TLX3 146 2.044 1- 4.175 0.05 NOTCH1/FBXW7 141 2.077 0.946-4.560 0.068 §PTEN/AKT aberrations 142 1.675 0.787-3.567 0.18 Multivariate analyses using Cox regression model n Hazard ratio 95% confidence interval P Male gender 141 2.910 1.117 – 7.577 0.029 TLX3 141 2.018 0.921 – 4.424 0.079 NOTCH-activating 141 2.588 1.083 – 6.183 0.032 §PTEN/AKT aberrations 141 3.407 1.254-7.400 0.014

Univariate and multivariate Cox regression analyses stratified for DCOG or COALL treatment protocols using relapse-free survival for various parameters that were significantly associated with poor relapse-free survival (see supplementary table S7). §Includes T-ALL patients that do not express PTEN protein while lacking PTEN aberrations, but does not include patient samples with PTEN aberrations only on the subclonal level.

Chapter 4

Lentiviral gene transfer into human and murine hematopoietic stem cells: size matters

Kirsten Canté-Barrett1, Rui D. Mendes1, Willem K. Smits1, Yvette M. van Helsdingen- van Wijk1, Rob Pieters2, and Jules P.P. Meijerink1

1Department of Pediatric Oncology/Hematology, Erasmus MC Rotterdam-Sophia Children’s Hospital, Rotterdam, the Netherlands 2Princess Máxima Center of Pediatric Oncology, Utrecht, the Netherlands

BMC Res Notes. 2016 Jun 16;9:312

Abstract Contemporary biomedical research increasingly depends on techniques to induce or to inhibit expression of genes in hematopoietic stem cells (HSCs) or other primary cells to assess their roles on cellular processes including differentiation, apoptosis, and migration. Surprisingly little information is available to optimize lentiviral transduction of HSCs. We have therefore carefully optimized transduction of murine and human HSCs by optimizing vector design, serum-free virus production, and virus quantitation. We conclude that the viral RNA length, even in relatively small vectors, is an important factor affecting the lentiviral gene transfer on the level of both the virus production and the cellular transduction efficiency. Efficient transfer of large gene sequences into difficult-to-transduce primary cells will benefit from reducing the lentiviral construct size.

Introduction Human immunodeficiency virus-based lentiviral gene transfer has been embraced in contemporary laboratory practice as an efficient procedure to shuttle gene-encoding RNA molecules into target cells, where they are reverse-transcribed and integrated into the host genome. Third-generation lentiviral vector systems have proven to be safe methods in gene therapy with very low risks of ongoing integrations in the host genome or generation of replication-competent viral particles.[1, 2] Lentiviruses infect both dividing and non-dividing cells, making them ideally suited to transduce human and murine hematopoietic stem cells (HSCs).[3- 6] Many fields of research often require lentiviral constructs that drive gene expression from a promoter as well as a fluorescent reporter expressed from an internal ribosomal entry site or secondary promoter. The virus production (tested for vectors that encode viral RNA ranging from 4-7.5 kb in length)[7] and efficiency to transduce adherent cell lines seems dependent on the size of the lentiviral vector that encodes for the viral RNA. Vectors with viral RNA ranging from 5- 9 kb generally tested decent, whereas those ranging from 10-18 kb transduced very poorly.[8] However, the nature of the target cell (cell line or primary cells) is crucial and most of the studies published to date have not addressed the transduction efficiency of primary cells. Additionally, it is important to control the production process of lentiviral particles (in HEK293T cells), and the quantitation method to determine the number of competent viral particles produced. Since little information is available regarding these variables and their effects on lentiviral transduction, we carefully optimized lentiviral transfer by optimizing vector design, serum-free virus production and quantitation, as well as by optimizing transduction of murine and human HSCs.

Methods

Cloning For fast, highly flexible and reliable cloning purposes, we have adapted the third-generation self- inactivating lentiviral LeGO-iC2 vector[9] into a Gateway compatible destination vector (LeGO- DEST) by replacing the ApaI/PciI 2kb insert with the Gateway ccdB cassette (Life Technologies). We assembled lentiviral expression constructs in which we cloned a promoter, a gene, a Thosea asigna virus 2A (T2A) element,[10] and the blue fluorescent protein reporter (mTagBFP). These, in combination with other lentiviral elements, are flanked by long terminal repeats and encode for the viral RNA. Upon translation, the gene and BFP moieties are efficiently separated by T2A cleavage in target cells (Figure 1).

Cell line The human T-cell leukemia cell line JURKAT (DSMZ, #ACC-282) identity was confirmed by DNA fingerprinting and cells were regularly tested for mycoplasma contamination.

Virus production, concentration and quantification We optimized HEK293T transfection in DMEM supplemented with 10% serum using X- tremeGENE HP DNA Transfection Reagent (Roche, #06 366 236 001) to produce vesicular stomatitis virus-G pseudotyped virus particles without the addition of serum (low-serum Opti- MEM I with Glutamax: Life Technologies, #51985-026), in batches of 40 ml (harvest twice, with 24-hour intervals, from two confluent 14-cm dishes starting two days after transfection, see Supplementary Protocols). Low serum levels minimize the risk of premature differentiation of HSCs that are subjected to lentiviral transduction. Furthermore, fetal bovine serum can influence transduction,[11] and we found that increasing amounts of serum (ranging from 0% to 10%) negatively affects transduction rates of human HSCs, perhaps due to the aggregation of virus particles in the presence of serum proteins (data not shown). In relation to the quantitation of intact viral particles, the commonly used p24 protein ELISA method overestimates the number of functional viruses by the detection of incomplete, transduction-deficient viral particles as well as soluble p24 protein in the production medium.[12] Quantitative RT-PCR of encapsulated viral RNA particles is a valuable alternative. We therefore calculate the number of viral particles that we produce by quantification of viral RNA copies using RT-qPCR (two RNA copies per viral particle, see Supplementary Protocols). RT-qPCR is performed using primers flanking the cPPT region: 5’-AGGTGGAGAGAGAGACAGAGAC-3’ and 5’-CTCTGCTGTCCCTGTAATAAAC-3’ 79

Human CD34+ HSC transduction Human CD34+ HSCs were positively selected from umbilical cordblood (Miltenyi Biotec, #130- 100-453) and stimulated for 16-20 hours at a concentration of 1x106 cells/ml in X-VIVO 10 (Lonza, #BE04-743Q) supplemented with 50 ng/ml rhSCF (R&D, #255-SC), 20 ng/ml rhTPO (R&D, #288-TP/CF) and 50 ng/ml rhFlt3L (Miltenyi Biotec, #130-093-855). Prior to transduction, add protamine sulfate (Sigma, #P4020-1G) to the cells to a final concentration of 4 µg/ml and pipet the concentrated virus (IVSS VIVASPIN 20 centrifugation concentration columns, Sartorius AG, Sigma-Aldrich, #Z614653-48EA) into a 50 µg/ml retronectin (r-Fibronectin CH-296: TaKaRa, #T100A)-coated 96-well plate (Falcon, #351172). Add HSCs on top of the virus to a final volume of 200 µl/well and mix by gently tapping the plate. Spinoculation: centrifuge the cells in the virus- containing medium at 1800 rpm, 32ºC for 1 hour. Incubate the transduced cells at 37ºC, 5%CO2 for 24 hours before further use.

Results We set out to relate the length of the viral RNA to the efficiency of virus production as well as to the potency of these viral batches to transduce the T-cell acute lymphoblastic leukemia line JURKAT. Our optimized lentiviral particle production consistently leads to near equal yields for consecutive batches produced from the same lentiviral construct (average variation of 4.3 fold (range 1.3-12 fold) for repetitive viral batches from 10 different constructs). We observed an inverse exponential correlation between the length of the viral RNA encoded by the construct and the number of viral particles produced by HEK293T cells (Figure 2). RT-qPCR in combination with functional titration experiments on a cell line reliably determines the number of viral particles required to efficiently transduce target cells. We investigated transduction efficiencies for virus batches of three different viral RNA lengths: 4215, 5190 and 5750 bases. For each construct, three independent viral batches were produced. Transduction experiments using serial dilutions of each viral batch were performed on JURKAT cells. Four to six days after transduction, the percentage of BFP-positive (transduced) cells was determined (Figure 3A, representative curves for three independently produced virus batches of each construct). While the intensity of BFP signal was highest for the virus with the shortest viral RNA and lower for longer constructs, in each case the transduced population was easily distinguished from non- transduced cells (inset in Figure 3A). The gene of interest encodes a protein with the C-terminal T2A that separates it from the BFP (Figure 3B). The percentage of transduced JURKAT cells remained unchanged for over three weeks of culture (not shown), reflecting stable integration of the viral genome in the host DNA. All independently produced batches from the same lentiviral 80

construct were virtually equally efficient to transduce JURKAT cells, indicating that the optimized production, quantitation and transduction procedures are very robust and reproducible. The transduction efficiency was highest for viruses with the shortest viral RNA sequence, and became progressively lower with increasing length. This is also true for all other virus batches produced with viral RNA lengths varying from 4 to 9 kb. In general, more viral particles with longer viral RNAs seem to be required to achieve equal transduction percentages in target cells compared to viruses with shorter viral RNAs.

We then tested the efficiency of these viral batches to transduce primary human CD34+ HSCs and murine ‘lineage-negative’ bone marrow (Lin- BM) cells. To achieve comparable transduction rates for JURKAT, human HSCs and mouse Lin- BM, a thousand-fold more virus particles of the 4215 bp vector are required for the primary cells than JURKAT (Figure 3C). This difference becomes even higher (up to 104) for viruses with larger viral RNAs (5190 and 5750 bases). Thus, the viral RNA size moderately, albeit significantly, affects the transduction rate of a cell line such as JURKAT, but greatly influences transduction efficiencies of freshly isolated human HSCs and murine Lin- BM cells. Transduced human HSCs cultured on stromal support cells retained an equal percentage of BFP-positive cells over the course of three weeks, indicating stable integration (data not shown). To investigate whether specific sequences can influence transduction efficiency, we generated new vectors harboring double or triple BFP sequences (5105 and 5995 bp, respectively; Figure 1). These vectors only differ in size from the single BFP construct, but contain the same sequence. Also for these viruses, the transduction efficiency in JURKAT was highest for the shortest vector and reduced with increased length (Figure 4).

Discussion We conclude that while larger viral RNA size negatively affects both virus production and transduction of target cells, other factors can also influence the transduction efficiency (e.g. sequence). This is evident from the observation that the 5750 bp vector (containing the 1535 bp gene) revealed lower transduction efficiency than the slightly larger triple BFP vector (5995 bp). The transduction efficiency of human or mouse stem cells decreases tremendously for viruses with viral RNAs approaching 6 kb or larger. Lentiviral vectors encoding smaller viral RNA sequences perform better and even a reduction of merely 600 bp (5750 versus 5190 bp) already improves transduction efficiency by more than three-fold (Figure 3). To produce more efficient lentiviruses, reducing the viral RNA backbone size by removal of non-essential sequences may be effective. Codon optimization alters the gene sequence without affecting the protein 81

sequence and may also increase transduction efficiency. Additionally, the development of smaller reporter genes or complete removal of the reporter may further enhance the transduction efficiency. In the absence of a fluorescent reporter, integrated lentiviral constructs into the host genome or expression of lentiviral transgene mRNA can be quantified by qPCR[13, 14] or RT- qPCR,[15] respectively. In conclusion, size reduction of lentiviral constructs will facilitate efficient transfer of large gene sequences into difficult-to-transduce primary cells, and will be most helpful in many fields of (basic) research.

Ethics approval and consent to participate The use of human umbilical cord materials from healthy individuals was approved by the Medical Ethical Review Board of the Erasmus MC Rotterdam (MEC-2009-430) and in accordance with the Declaration of Helsinki. C57Bl/6 mice were housed under specific pathogen free conditions at the animal facility of Erasmus MC according to institutional guidelines. The use of murine bone marrow for the experiments has been approved by the Erasmus MC committee for animal welfare (DEC #103-12-02) and is in compliance with Dutch legislation.

Acknowledgements We thank Karin Pike-Overzet for helpful discussions. This work was supported by the Children Cancer Free Foundation (Stichting Kinderen Kankervrij); grants KiKa-2008-29 (RDM, WKS, and KC-B), KiKa-2013-116 (KC-B), and KiKa-2014-141 (YMH-W).

Authorship Contributions KC-B, RDM, WKS, and YMH-W performed experiments and/or analyzed data; KC-B and JPPM designed study and wrote manuscript; RP and JPPM supervised the study.

Conflicts of Interest Disclosure and Supplementary Information The authors declare no conflict of interest. Supplementary Protocols (.docx file) include protocols for virus production and concentration, viral RNA isolation and RT-qPCR, and human CD34+ HSC transduction.

References

1. Debyser Z. Biosafety of Lentiviral Vectors. Current gene therapy. 2003; 3:6. 2. Dull T, Zufferey R, Kelly M, Mandel RJ, Nguyen M, Trono D, Naldini L. A Third- Generation Lentivirus Vector with a Conditional Packaging System. Journal of virology. 1998; 72:11. 3. Naldini L, Blomer U, Gallay P, Ory D, Mulligan R, Gage FH, Verma IM, Trono D. In Vivo Gene Delivery and Stable Transduction of Nondividing Cells by a Lentiviral Vector. Science. 1996; 272:5259. 4. Miyoshi H, Smith KA, Mosier DE, Verma IM, Torbett BE. Transduction of Human Cd34+ Cells That Mediate Long-Term Engraftment of Nod/Scid Mice by Hiv Vectors. Science. 1999; 283:5402. 5. Naldini L. Lentiviruses as Gene Transfer Agents for Delivery to Non-Dividing Cells. Current opinion in biotechnology. 1998; 9:5. 6. Hong Y, Lee K, Yu SS, Kim S, Kim JG, Shin HY, Kim S. Factors Affecting Retrovirus- Mediated Gene Transfer to Human Cd34+ Cells. The journal of gene medicine. 2004; 6:7. 7. al Yacoub N, Romanowska M, Haritonova N, Foerster J. Optimized Production and Concentration of Lentiviral Vectors Containing Large Inserts. The journal of gene medicine. 2007; 9:7. 8. Kumar M, Keller B, Makalou N, Sutton RE. Systematic Determination of the Packaging Limit of Lentiviral Vectors. Human gene therapy. 2001; 12:15. 9. Weber K, Bartsch U, Stocking C, Fehse B. A Multicolor Panel of Novel Lentiviral "Gene Ontology" (Lego) Vectors for Functional Gene Analysis. Molecular therapy : the journal of the American Society of Gene Therapy. 2008; 16:4. 10. Szymczak AL, Vignali DA. Development of 2a Peptide-Based Strategies in the Design of Multicistronic Vectors. Expert opinion on biological therapy. 2005; 5:5. 11. Denning W, Das S, Guo S, Xu J, Kappes JC, Hel Z. Optimization of the Transductional Efficiency of Lentiviral Vectors: Effect of Sera and Polycations. Molecular biotechnology. 2013; 53:3. 12. Geraerts M, Willems S, Baekelandt V, Debyser Z, Gijsbers R. Comparison of Lentiviral Vector Titration Methods. BMC biotechnology. 2006; 6. 13. Barczak W, Suchorska W, Rubis B, Kulcenty K. Universal Real-Time Pcr-Based Assay for Lentiviral Titration. Molecular biotechnology. 2014. 14. Christodoulou I, Patsali P, Stephanou C, Antoniou M, Kleanthous M, Lederer CW. Measurement of Lentiviral Vector Titre and Copy Number by Cross-Species Duplex Quantitative Pcr. Gene therapy. 2016; 23:1. 15. Lizee G, Aerts JL, Gonzales MI, Chinnasamy N, Morgan RA, Topalian SL. Real-Time Quantitative Reverse Transcriptase-Polymerase Chain Reaction as a Method for Determining Lentiviral Vector Titers and Measuring Transgene Expression. Human gene therapy. 2003; 14:6.

Figure legends

Figure 1. Schematic representation of the elements cloned into our Gateway compatible LeGO- DEST vector. The different elements and their sizes (bp) that were cloned into our Gateway compatible LeGO-DEST vector to generate lentiviral expression vectors of different sizes between SIN-LTRs: 4215, 5190, and 5750 bp. SFFV: Spleen Focus Forming Virus promoter, T2A: Thosea asigna virus 2A element, BFP: blue fluorescent protein. Grey boxes indicate lentiviral elements in LeGO-DEST.

Figure 2. Relationship between the viral RNA length and production of lentiviral particles. The quantity of lentiviral particles that are produced (calculated using RT-qPCR) plotted as a function of the viral RNA length (kb). Each point in the plot represents an individual virus batch, each time produced using the pLEGO-DEST backbone and the same transfection method and producer cell line (see S1 Protocol). The viral RNA length is the length of the sequence flanked by the 5’ and 3’ SIN-LTRs in the lentiviral expression vector (see Figure 1).

Figure 3. Transduction efficiency of JURKAT, CD34+ human HSCs, and murine Lin- BM with lentiviral vectors of varying sizes. Transduction efficiency of different cell types as a function of the amount of virus particles, measured 4-6 days after transduction and expressed as the BFP+ percentage of the viable cells. Lentiviruses with three different viral RNA sizes (length between SIN-LTRs) are compared: 4215 (solid black lines), 5190 (dark-grey dashed lines), and 5750 bp (light-grey dashed lines). A. Triplicate transductions ±SD of JURKAT cells using serial dilutions of each lentivirus, representative of one of three individually produced virus batches. Inset: example flow cytometry plot displaying the BFP+ transduced fraction. B. Western blot probed with anti-2A antibody. Total lysates expressing the T2A-tagged proteins of the 4215 bp (BFP only, no T2A), 5190 and 5750 bp constructs, separated from BFP. *: non-specific signals. C. Triplicate transductions ±SD of human CD34+ HSCs (solid triangles) and murine Lin- BM (solid circles) with the three lentiviruses.

Figure 4. Transduction efficiency of JURKAT with independently generated and different lentiviral vectors of varying sizes. A. Transduction efficiency of JURKAT cells as a function of the amount of virus particles, measured 4-6 days after transduction and expressed as the BFP+ percentage of the viable cells. Lentiviruses with three different viral RNA sizes (length between SIN-LTRs) are compared: 4215 (1xBFP; black), 5105 (2xBFP; blue), and 5995 bp (3xBFP; red). Triplicate transductions ±SD using serial dilutions of each lentivirus, representative of one of

three individually produced virus batches. B. Average transduction percentage of the different viruses, measured at the number of virus particles that yielded 75% in the ‘1xBFP’ (4215 bp) vector (indicated by dashed vertical lines in Figure 3 and 4A)

Figure1

Figure 2

Figure 3

Figure 4

Chapter 5

Downregulation of CD44 functionally defines human T-cell lineage commitment

Rui D. Mendes1,5, Kirsten Canté-Barrett1,5, Yunlei Li1, Eric Vroegindeweij1, Karin Pike-Overzet2, Tamara Wabeke3, Anton W. Langerak3, Rob Pieters1,4, Frank J.T. Staal2, and Jules P.P. Meijerink1

1Department of Pediatric Oncology/Hematology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands; 2Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands; 3Department of Immunology, Erasmus Medical Center, Rotterdam, The Netherlands; 4Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands. 5Co-first author

Manuscript submitted

Abstract Human T-cell development is less well studied than its murine counterpart due to the lack of genetic tools and the difficulty of obtaining cells and tissues. Here we report the transcriptional landscape of 11 immature, consecutive human T-cell developmental stages. The changes in gene expression of cultured stem cells on OP9-DL1 match those of ex vivo isolated human thymocytes. These analyses led us to define evolutionary conserved gene signatures that represent pre- and post- αβ T-cell commitment stages. We found that loss of CD44 marks human T-cell commitment in early CD7+CD5+CD45dim cells, before the acquisition of CD1a surface expression. The CD44-CD1a- post-committed thymocytes have initiated in frame TCR rearrangements and have completely lost the capacity to develop into myeloid, B- and NK-cells, unlike uncommitted CD44+CD1a- thymocytes. Therefore, loss of CD44 represents a previously unrecognized stage that defines the earliest committed T-cell population in the human thymus.

Introduction T-cell development in the thymus is a complex process that depends on sequential transcriptional and epigenetic changes leading to T-cell lineage commitment while suppressing alternative cell fates.(Rothenberg et al., 2008; Zhang et al., 2012) T-cell development has been extensively studied in mice, while human T-cell development is less well defined due to limited availability of human thymus material and inherent genetic diversity. Research has focused on mimicking the thymus environment using in vitro differentiation cultures starting with hematopoietic stem cells (HSCs) derived from human cord blood or bone marrow. Historically, human-mouse hybrid fetal thymic organ cultures (FTOC) have been used to functionally define the various stages of human T-cell development.(Plum et al., 1994) Later, OP9-DL1 co-cultures have been proven more useful to study T-cell differentiation.(Schmitt and Zuniga-Pflucker, 2002) The expression of the Delta-like 1 ligand on bone marrow-derived stromal cells from op/op M- CSF deficient mice(Yoshida et al., 1990) induces NOTCH signaling in target cells of the hematopoietic lineage. NOTCH signaling promotes T-cell differentiation while inhibiting B-cell differentiation.(Radtke et al., 2004) The differentiation of HSCs in this co-culture recapitulates human in vivo T-cell development as measured by the successive acquisition of CD7, CD5, CD1a, and CD4+CD8 surface markers.(Awong et al., 2009; La Motte-Mohs et al., 2005) During development in the thymus, early T-cell precursors migrate within the cortex from which the positively selected CD4 and CD8 double positive (DP) thymocytes migrate to the

medulla. As a result, developing thymocytes encounter specific signals at specific locations in the thymus.(Halkias et al., 2014) The OP9-DL1 in vitro co-culture lacks the proper thymus architecture required for T-cell development. Hence, it supports the development of early T-cell development until the DP stage, with few cells reaching the CD8 single-positive (SP) stage. These few CD8SP cells have most likely been subject to positive selection based on MHC class I expression on OP9-DL1 cells.(Dervovic and Zuniga-Pflucker, 2010) Despite the presence of these rare SP cells it is generally believed that the absence of proper antigen-presenting cells and a thymus environment impair complete positive and negative selection processes in OP9- DL1 co-cultures. Early T-cell progenitors (ETPs) are pre-committed, multi-potent thymocytes that retain the ability to develop into hematopoietic cells other than the T-cell lineage including NK-cells, B- cells, and cells of the myeloid and erythroid lineages.(Bell and Bhandoola, 2008; Bhandoola et al., 2007; Weerkamp et al., 2006) Fully-committed thymocytes have lost multi-potency and have undergone RAG1/2-mediated T-cell receptor (TCR) alpha and beta chain rearrangements. Human CD4/8 double-negative (DN) thymocytes acquire CD1a at the proliferation stage when cells are committed to the T-cell fate.(Dik et al., 2005) The human hematopoietic stem cell marker CD34 is gradually lost throughout development but it is a poor marker for ‘stemness’ of pre-committed thymocytes as it remains expressed (albeit dimly) on most CD1a+ T-cell committed thymocytes.(Dik et al., 2005; Weerkamp et al., 2006; Weerkamp et al., 2005) Until now, upregulation of CD1a has been used to define human T-lineage commitment,(Weerkamp et al., 2006) but the human DN thymocyte maturation stages and the exact T-cell commitment point had not been clearly defined. In this study, we have generated gene expression profiles of sequential human T-cell differentiation stages, derived from umbilical cord blood stem/progenitor cells that have full multi- lineage differentiation potential and that were cultured on OP9-DL1 stromal cells. Comparison between these in vitro-derived signatures and the gene signatures from normal murine and human early T-cell development stages in the thymus reveals strong conservation of pre- and post-T-cell commitment transcriptional profiles. From this analysis, we found that loss of human CD44 expression at the surface membrane of early DN thymocytes marks the loss of alternative cell fate decisions, initiation of TCRB recombination, and T-cell commitment.

Results Consecutive stages of human in vitro T-cell differentiation represent two major gene signatures 90

CD34+ HSCs isolated from human umbilical cord blood were cultured on OP9-DL1 stromal cells. At various time points during a 27-day co-culture (performed in biological triplicate), we sorted seven distinct T-cell populations as progeny from pooled CD34+ HSCs from multiple cord blood donors (Fig. 1A). We performed microarray gene expression analysis on a total of 26 OP9-DL1- generated T-cell populations and three CD34+ HSC starting pools. Principal component analysis (PCA) revealed three distinct clusters, one of which consists of the CD34+ stem/progenitor starting populations (Fig. S1A). After filtering for probes with high variance (top 5%) and high log2 expression (>8), the expression profile was trimmed to 1387 genes (2179 probesets) that retain the three PCA clusters (Fig. 1B, Table S1). One cluster represents the gene expression profile of CD34+ cells. The other two developmental clusters characterize ‘early’ T-cell progenitor (ETP) populations (including the CD7+ and the day 7 and day 12 CD7+CD5+ sorted populations) and ‘late’ T-cell differentiation populations (including the day 18 CD7+CD5+, CD7+CD5+CD1a+, CD4/8 DP, and SP sorted populations). The ‘early’ and ‘late’ clusters represent a major change in transcriptional programs (Fig. 1B,C). Of note, the CD7+CD5+ population (in orange) is divided over both clusters (Fig. 1B); those fractions that were sorted on day 7 and day 12 of the co- culture cluster with the early CD7+ population, whereas those sorted on day 18 fall in the ‘late’ cluster. Therefore, this CD7+CD5+CD1a- population is not homogeneous and represents two distinct differentiation stages that dramatically differ in their transcriptional programs. The 1387 genes of our expression profile harbor 16 different gene signatures of co- expressed genes (Fig. 1C,D, Table S1). Based on gene set enrichment analysis (GSEA) we identified five signatures that are differentially expressed between the early and late T-cell differentiation populations (Figure 1E). Signatures #7 and #8 are enriched for genes that are highly expressed in the early T-cell progenitor stages, and downregulated at later T-cell differentiation stages. Signatures #2, #15 and #16 are enriched for genes that become expressed at later T-cell differentiation stages. These five gene signatures—comprising 547 genes in total—faithfully distinguish the early and late T-cell differentiation populations (Fig. 2A, Fig. S1B, Table S1). Early T-cell differentiation signatures #7 and #8 include transcription (co)factors that are important for HSC/ETP maintenance and/or lineage determination, such as MEIS1, LMO2, MEF2C, LYL1, and HHEX.(Goodings et al., 2015; Yui and Rothenberg, 2014; Zohren et al., 2012) Genes encoding transcription factors that are responsible for T-cell identity (including GATA3, TCF7, BCL11B) and TCR rearrangements (RAG1/2) are present in late signatures #2, #15 or #16. Therefore, these five signatures contain essential genes that are associated with transcriptional programs required for stem/progenitor identity, T-cell specification and lineage commitment.

The in vitro gene expression signatures recapitulate in vivo signatures of pre- and post- T-cell committed thymocytes We then compared our gene expression signatures to those from murine and human ex vivo sorted thymocyte subsets.(Dik et al., 2005; Mingueneau et al., 2013; Yui and Rothenberg, 2014) We performed GSEA to determine the enrichment of our five gene signatures in the dataset of consecutive T-cell development populations from murine thymi, generated by the Immunological Genome Consortium.(Mingueneau et al., 2013) As a result, we demonstrate that our “early” signatures #7 and #8 are significantly enriched for genes expressed in pre-committed murine subsets ranging from long-term HSCs to DN1-DN2a stages (Fig. 2B; false discovery rate (FDR)- corrected p-value is q<0.001). Known pre-commitment genes such as HHEX, KIT, MEF2C, LYL1 are among the top-10 genes in our early signatures (Fig. 2B: left panel, Table S2). Our “late” T-cell development gene signatures #2, #15, and #16 are significantly enriched for genes expressed in post-committed murine thymocyte subsets (DN2b and later subsets) and include BCL11B, TCF7, LEF1, GATA3 and various molecules associated with TCR signaling (FDR q=0.028, Fig. 2B: right panel, Table S2). Next, we applied our in vitro gene signatures onto the gene expression data of human ex vivo sorted thymocyte populations.(Dik et al., 2005) Interestingly, similar maturation stages of in vitro and ex vivo populations group together in hierarchical clustering (not shown) and our gene signatures that discriminate pre- and post- commitment also divide the ex vivo sorted human thymocytes into pre-committed (the CD1a- populations(Dik et al., 2005)) and post-committed populations (CD1a+ and beyond; Fig. 2C and Table S3).(Dik et al., 2005) These results show that the pre- and post-commitment programs of murine and human in vivo T-cell development are highly conserved in T-cells cultured from HSCs on OP9-DL1 stromal support.

Loss of CD44 surface expression marks T-cell commitment in early human thymocytes Further examination of the genes expressed in the ex vivo CD34+CD1a- human thymocyte sorted populations(Dik et al., 2005) revealed that certain post-commitment genes (including PTCRA, RAG1, RAG2 and DNTT (known as TDT)) are already expressed, suggesting that thymocytes prior to their expression of CD1a have initiated TCRB rearrangement and committed to the αβ T-lineage (Fig. 2C, Table S3). From our pre- and post-commitment gene signatures, we searched for surface antigens that are differentially expressed between pre and post commitment. In doing so, we observed that expression of CD44, CD33, CD34, CD63, CD69, and CD300LF were reduced before the CD1a+ stage (Fig. 1D). In order to investigate whether

loss of CD44 protein marks human T-cell commitment, we examined CD44 surface expression on differentiating T-cells in the OP9-DL1 system and observed that CD44 is lost from CD5+ T- cells before they express CD1a (Fig. 3A). We sorted subsets at days 12 and 18 from two independent OP9-DL1 co-cultures with the inclusion of CD44 as an additional sorting marker, and were able to discriminate CD5+CD44+CD1a- (E1), CD5+CD44-CD1a- (E2) and CD5+CD44- CD1a+ (F) populations (Fig. 3A). Microfluidic cards-based RT-qPCR analysis of 47 pre- or post- commitment genes strongly supported a pre-commitment profile in sorted populations A through E1, and a T-lineage commitment profile in populations E2 and F (Fig. 3B). Therefore, T-cell commitment of differentiating T-cells cultured on OP9-DL1 is associated with loss of CD44 before the acquisition of CD1a surface expression. Next, we asked whether CD44 represents a pre-commitment marker in ex vivo human thymocytes. Thymi from four independent immunologically healthy donors were used to sort CD7+CD5+CD45dim immature thymocytes (following depletion of CD3, CD4, CD8, and CD19 cells). Within the CD7+CD5+CD45dim cells, we sorted three populations: CD44+CD1a- (population I, equivalent to the OP9-DL1 co-cultured population E1), CD44-CD1a- (II, equivalent to E2), and CD44-CD1a+ (III, equivalent to F) (Fig. 3C). CD44 becomes highly re-expressed on mature thymocytes, requiring depletion of the large bulk of mature cells that constitutes approximately 99% of all thymocytes. CD44 expression on early, pre-committed thymocytes is dim in comparison to CD44 expression on mature T-cells,(Marquez et al., 1995) and correlates with CD45 expression levels (Fig. S2A). CD45dim thymocytes predominantly consist of developmentally early cells including CD4/8 DN, CD4 immature single positive (ISP) and some CD3- DP cells, whereas CD45bright thymocytes mostly consist of CD3+ DP, CD4 SP and CD8 SP cells (Fig. S2A). Mature B-, myeloid, and NK-cells are present in very low numbers in the thymus,(Weerkamp et al., 2005) and these mature CD3/4/8 triple-negative cells are also CD45brightCD44bright (Fig. S2B). Therefore, immature (CD34+)CD7+CD5+ thymocytes were sorted from the CD45dim population, and further sorted into sub-populations I-III based on CD44 and CD1a expression. Hierarchical cluster analysis of the gene expression levels of the panel of 47 pre- and post commitment genes identifies population I (CD44+) as pre-committed thymocytes, whereas populations II and III (CD44-) represent post-committed thymocytes (Fig. 3D). Therefore, loss of CD44 marks the transition to human T-cell commitment in vivo, consistent with our in vitro data. Of note, expression of the stem-cell marker CD34 gradually decreased in populations I-III but was still present on CD1a+ thymocytes (population III, Fig. 3C), in line with previous observations.(Dik et al., 2005; Weerkamp et al., 2006) Therefore, loss of CD34 expression is not directly associated with T-cell commitment.

Loss of CD44 functionally marks human T-cell commitment αβ T-cell committed thymocytes have initiated or completed TCRB gene rearrangements. To further validate commitment in human thymocyte populations II and III, we analyzed Dβ-Jβ and Vβ-Jβ recombination events using GeneScan analysis(van Dongen et al., 2003). Analysis of sorted CD44+ uncommitted thymocytes (population I) did not reveal detectable rearrangements (Fig. 4A). In contrast, incomplete Dβ-Jβ and Vβ-Jβ recombination events were identified in the earliest population of post-committed CD44-CD1a- thymocytes (II) with a more complete spectrum of full recombinations present in CD44-CD1a+ thymocytes (III) (Fig. 4A). To provide functional evidence that loss of CD44 marks T-cell commitment, we investigated the intrinsic potential of alternative cell fate decisions in the three sorted human thymocyte populations I-III. We co-cultured thymocyte populations I (pre-committed), II and III (post-committed) on OP9- DL1, OP9-GFP (control), and a mixture of OP9-DL1/OP9-GFP stromal cells. These cultures were performed in the presence of cytokines that support differentiation into various hematopoietic lineages. After seven days of co-culture, microscopic inspection already revealed the inability of committed thymocyte populations II and III to proliferate in co-cultures with OP9- GFP stromal cells that do not support T-cell differentiation (Fig. S3). After ten days of co-culture under various stromal support conditions we determined the total numbers of T-, NK-, B-, and myeloid cells based on lineage markers CD5 and CD1a (T), CD56 and CD94 (NK), CD19 (B), and CD33 (myeloid) (Fig. 4B, C). The pre-committed thymocytes (population I) proliferated well and differentiated into cells of all four lineages on OP9-DL1 support. This pre-commitment population also differentiated into B-, myeloid and NK-cells—but not T-cells—when cultured on OP9 control cells. In contrast, committed thymocyte populations II and III gave rise to CD5+CD1a+ T-cells only, indicating that these populations have lost the potential for alternative cell fates (Fig. 4C). The fact that both populations II (CD44-CD1a+) and III (CD44-CD1a-) fully adopted the T-cell fate illustrates that loss of CD44—rather than the acquisition of CD1a—is the first surface marker that identifies post-committed human thymocytes.

Discussion The early stages of human T-cell development have been investigated(Spits, 2002) without or with gene expression profiling,(Dik et al., 2005) using FTOC cultures,(Pike-Overzet et al., 2007; Plum et al., 1994; Res et al., 1996) and more recently co-cultures on OP9-DL1.(Van Coppernolle et al., 2009) During the last decade, the OP9-DL1 in vitro co-culture system has been used widely to study T-cell development.(de Pooter and Zuniga-Pflucker, 2007) The use of human 94

hematopoietic progenitors in this culture system offers a relatively easy and efficient tool to translate studies from in vivo mouse models to the human situation.(Van Coppernolle et al., 2009) Sequential acquisition of T-cell specific surface markers recapitulates T-cell development from bone marrow or cord blood hematopoietic progenitors to the DP stage.(Awong et al., 2009) Here, we have sorted subsets at different stages during early T-cell differentiation and demonstrated conservation of early T-cell transcriptional profiles including pre- and post-T-cell commitment programs. We identified loss of CD44 expression in immature thymocytes as a marker for T-cell commitment that precedes acquisition of CD1a. Our results demonstrate that population II (CD44-CD1a-) has initiated TCR Dβ-Jβ and Vβ-Jβ rearrangements and is already completely T-lineage restricted. These results explain why the CD34+CD1a- thymocyte population from previous studies—considered to be uncommitted thymocytes—is in fact heterogeneous with mixed CD44 expression.(Awong et al., 2009; Dik et al., 2005) The multi- lineage potential of CD34+CD44+ human thymocytes has been suggested,(Marquez et al., 1995) but this study places CD44 expression in the context of other T-cell development markers, and more importantly provides the first functional evidence that loss of CD44 marks human T-cell commitment in vivo. Similar to human early T-cell development, the predominant transcriptional change in murine T-cell development—well before the DP stage—is from pre- to post- αβ T-cell commitment.(Mingueneau et al., 2013; Zhang et al., 2012) In murine thymocytes, this well- characterized transition occurs in the second CD4/8 double-negative stage (DN2; CD44+CD25+), before the TCR checkpoint in DN3 (CD44-CD25+).(Bhandoola et al., 2007; Godfrey et al., 1994) T-cell commitment is defined by the transition from DN2a to DN2b that is marked by the initial reduction of CD44 surface expression.(Mingueneau et al., 2013; Zhang et al., 2012) The fact that the initial reduction of CD44 in the transition from DN2a to DN2b thymocytes also marks T- cell commitment in mice suggests a functional role for CD44 in pre-committed thymocytes that is conserved in humans. CD44 is an glycosylated adhesion and migration molecule with several isoforms due to alternatively spliced exons in the extracellular domain.(Zoller, 2015) The standard CD44 isoform (CD44s) is expressed on hematopoietic (stem) cells (as well as many other cell types) and is required for HSC niche formation and quiescence during early hematopoietic development. CD44 has gained clinical interest because CD44s and variant isoforms (CD44v) feature ‘stemness’ potential of many cancer types, including leukemia. Currently, multiple anti-CD44- based therapeutic approaches are investigated for their ability to target leukemia- or cancer- initiating cells (LIC/CIC) with this ‘stemness’ potential.(Zoller, 2015) In addition to its expression

on hematopoietic stem/progenitor and ETP cells, CD44 is highly re-expressed on mature lymphocytes (Fig. 5) and functions in the homing to secondary lymphoid organs. In the thymus, CD44 and its cleavage by metalloproteinases also direct the migration of mature thymocytes within the medulla as well as the exit from the medulla.(Vivinus-Nebot et al., 2004) The rare population of uncommitted CD44+ ETPs is present in subcapsular clusters in the cortex of the thymus, suggesting that CD44 also directs thymocyte precursors from the bone marrow to the thymus, entry into the thymus, and potentially intrathymic migration during early T-cell development.(de la Hera et al., 1989; Horst et al., 1990) By adding CD44, we have expanded the immunophenotypes of the different phases of early T-cell development in the human thymus (Fig. 5). CD44dimCD45dim human thymocytes reflect pre-committed ETP cells that are mainly characterized by the expression of transcription factors from alternative blood lineages, multi-lineage potential, and the absence of TCRB rearrangements. Loss of CD44 marks T-cell commitment that coincides with the upregulation of T-cell specific transcription factors (e.g. GATA3, BCL11B, TCF7), loss of alternative cell fate potential, and the initiation of TCRB rearrangements. Next, the thymocytes acquire CD1a and complete their TCRB rearrangements. Therefore, combining CD44 and CD1a surface markers in CD45dim thymocytes (in addition to other markers) is important in the characterization of pre- and post-committed human thymocytes and will help to better understand human T-cell development and T-cell commitment.

Materials and Methods Human umbilical cord blood and thymus samples Human umbilical cord blood (UCB) samples were obtained from consenting mothers after delivery at local hospitals. Mononuclear cells (MNCs) were isolated by Ficoll-Paque density centrifugation, washed and frozen in 10% dimethyl sulfoxide (DMSO) and 90% fetal bovine serum (FBS) for later use. Thymi were obtained as surgical tissue discards from infants 2-9 months of age undergoing cardiac surgery at Erasmus MC Rotterdam, after informed consent from the parents or legal guardians. The children did not have immunological abnormalities. Thymocytes were isolated by cutting the thymic lobes into small pieces and squeezing them through a metal mesh, and stored at -80oC until further analyses. Informed consents were in accordance with the Institutional Review Board of the Erasmus MC Rotterdam and in accordance with the Declaration of Helsinki.

Isolation of CD34+ cells from umbilical cord blood 96

Frozen MNCs were thawed, washed and labeled with MicroBeads conjugated to the monoclonal mouse anti-human CD34 antibody according to the manufacturer’s procedure (Miltenyi Biotec). The magnetic separation was performed twice using two MACS Columns (Miltenyi Biotec), consistently reaching a CD34+ purity of approximately 95%.

Antibodies Antibodies (with clone identification) used for flow cytometry: CD1a (HI149), CD3 (UCHT1), CD7 (M-T701), CD10 (HI10a), CD11b (ICRF44), CD13 (WM15), CD14 (MΦP9), CD20 (L27), CD22 (HIB22), CD33 (WM53), CD34 (8G12) (BD Biosciences), CD3 (BW264/56), CD4 (VIT4), CD5 (UCHT2), CD8 (BW135/80), CD19 (LT19), CD33 (AC104.3E3), CD34 (AC136), CD44 (DB105), CD45 (5B1), CD56 (REA196), CD94 (REA113) (Miltenyi Biotec).

OP9-DL1 co-cultures OP9-DL1 co-cultures were performed according to the original protocol.(Holmes and Zuniga- Pflucker, 2009) Briefly, CD34+ human HSC isolated from UCB were plated into 100mm or 145mm culture dishes (Greiner Bio-One B.V.), which were seeded with a monolayer of OP9-DL1 cells beforehand. The α-minimal essential medium (α-MEM) (Life Technologies) used for culture was supplemented with 20% fetal bovine serum (FBS) (Integro B.V.) plus 10 U/mL Penicillin, 10 µg/mL of streptomycin, and 0.025 µg/mL fungizone (PSF) (Life Technologies). Recombinant human stem cell factor (SCF) (10 ng/mL) (R&D systems), FLT3L (5 ng/mL) (Miltenyi Biotec), and interleukin-7 (IL7) (2 ng/mL) (Miltenyi Biotec) were added at the initiation of coculture and every 2-3 days during transfer of HSC-derived cells onto new OP9-DL1 plated cells. Co-cultures were disaggregated by vigorous pipetting and passaged through a 40-µm filter to reduce stromal cell line aggregates and eliminate contaminating OP9-DL1 cells. Multi-lineage differentiation experiments were performed with the following modifications: recombinant human SCF (10 ng/ml), FLT3L (5 ng/ml), IL7 (5 ng/ml), and IL15 (10 ng/ml) were added every 3-4 days to half of the medium that was refreshed without disturbing the cells. A continuous 10-day OP9- co-culture was initiated with 1000 OP9 (-DL1, -GFP, or 1:1 mix) cells/well plated in 96-well plates one day before adding 4000/well of the sorted thymocytes. OP9-GFP and OP9-DL1 cell lines were kindly given to us by Dr. Zúñiga-Pflücker; the identity of the cell lines was confirmed by DNA fingerprinting and cells were regularly tested for mycoplasma contamination.

TCRB rearrangements

In-frame Dβ-Jβ and Vβ-Jβ gene rearrangements were determined using the BIOMED-2 multiplex PCR and visualized using GeneScan.(van Dongen et al., 2003) Primers for the Jβ1 cluster were hexachloro-6-carboxy-fluorescein (HEX)-labeled (blue traces), and primers for the Jβ2 cluster were 6-carboxy-fluorescein (FAM)-labeled (green traces).

Cell sorting and RNA preparation Throughout the differentiation of HSC towards the T-cell lineage, we sorted T-cell fractions at various time points (days 0, 7, 12, 18, and 27) based on the expression of cell surface markers including CD45, CD34, CD7, CD5, CD1a, CD4 and CD8. Cell sorting was performed using the FACSAria II (BD Biosciences), flow-cytometry was performed using the MACS Quant (Miltenyi Biotec), and data analysis was performed using the FlowJo software (TreeStar Inc.). Sorted populations were collected, washed twice in PBS, lysed in buffer RLT (Qiagen) plus 1/100 β- mercaptoethanol and stored at -80oC. RNA was isolated using RNeasy Micro Kit according to the manufacturer’s protocol (Qiagen).

Affymetrix gene expression arrays The quality control, RNA labeling, hybridization and data extraction were performed at ServiceXS B.V. (Leiden, the Netherlands). RNA concentration was measured using the Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies). The RNA quality and integrity was determined using Lab-on-Chip analysis on the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.) and/or on the Shimadzu MultiNA RNA analysis chips (Shimadzu Corporation). Biotinylated cRNA was prepared using the Affymetrix 3’ IVT Express Kit (Affymetrix) according to the manufacturer’s specifications with an input of 100ng total RNA. The quality of the cRNA was assessed using the Shimadzu MultiNA in order to confirm if the average fragment size was according to Affymetrix’ specifications. Per sample, 7.5μg cRNA of the obtained biotinylated cRNA samples was fragmented and hybridized in a final concentration of 0.0375 μg/μl on the Affymetrix HT HG U133+ PM (Affymetrix). After an automated process of washing and staining by the GeneTitan machine (Affymetrix) using the Affymetrix HWS Kit for GeneTitan (part nr. 901530), absolute values of expression were calculated from the scanned array using the Affymetrix Command Console v3.2 software. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus(Edgar et al., 2002) and are accessible through GEO Series accession number GSE79379 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79379).

Taqman array microfluidic cards Reverse-transcriptase (RT) reactions (Promega) were performed to convert mRNA into cDNA. The real-time PCR (qPCR) reactions were performed by Taqman Custom arrays that were pre- designed according to the manufacturer instructions (Applied Biosystems). TaqMan gene expression assay targets were based on selected primers that were pre-loaded into each of the wells of a 384-well Taqman array card. TaqMan Gene Expression Assays consist of a pair of unlabeled PCR primers and a TaqMan probe with a FAM or VIC dye label on the 5’ end and minor groove binder (MGB) and nonfluorescent quencher (NFQ) on the 3’ end. For the reaction, cDNA samples were diluted and mixed in 1:1 ratio with Taqman Fast Universal PCR Master Mix (Applied Biosystems). Relative levels of gene expression are determined from the fluorescence data generated during PCR using the 7900HT Fast Real-Time PCR System Relative Quantitation software (Applied Biosystems).

Bioinformatics Affymetrix gene expression CEL files were processed using Partek® Genomics Suite® 6.6. Robust Multi-array Average (RMA) (Irizarry et al., 2003) was applied prior to further analysis. In brief, the intensity levels were quantile normalized after background correction. Probeset-level expression values were summarized by median polish approach and eventually log2 transformed. Probesets were filtered for high variance (top 5%) and log2 expression values (>8), which resulted in 2179 probesets. Using the annotation file of the array, these probesets were then summarized into 1387 genes by taking the median of all probesets across a gene. Principle component analysis (PCA) and hierarchical clustering were done using Partek® Genomics

® Suite 6.6. Log2 expression values were standardized into z-scores prior to the clustering analysis. Pearson dissimilarity and Euclidean distance were used as distance metric. Gene set enrichment analysis (GSEA) (Subramanian et al., 2005) was performed using GSEA software downloaded from Broad Institute (http://www.broadinstitute.org/gsea). For the OP9-DL1 in vitro dataset, the GSEA input gene list consisted of 1387 genes that were grouped into the 16 gene signatures identified by the clustering analysis. The T-cell populations were denoted as early or late T-cell program according to their gene expression profile similarities during T-cell differentiation. Gene sets with FDR q≤0.25 were considered significantly correlated to the class distinction.(Subramanian et al., 2005) The murine in vivo microarray data was obtained through access to the Immunological Genome Project data, GEO accession code: GSE15907.(Mingueneau et al., 2013) For analysis of this in vivo dataset, the GSEA input gene list consisted of 547 genes enclosed in the 5 signatures that form the T-cell development gene

expression signature. The human in vivo microarray data was obtained at http://www.ebi.ac.uk/miamexpress, MIAME accession no. E-MEXP-337. Hierarchical clustering on the human thymocyte populations was performed using only 399 out of 547 genes from the T-cell development gene signature as the remaining 148 genes were not profiled in the human dataset. Relative levels of gene expression data generated by the Taqman array microfluidic cards were processed using Partek® Genomics Suite® 6.6. The sorted in vitro populations (A-F) were subject to ANOVA analysis to remove day and pool effects prior to the hierarchical clustering. Standardized z-scores of the expression values in 47 selected T-cell development genes were clustered using Pearson dissimilarity measure and average linkage. These 47 genes are a subset of the 1387 genes with high variation and expression levels in our in vitro T- cell differentiation dataset. The same genes and clustering method were used for the human thymocyte populations (I-III) dataset. For this dataset, the expression values obtained by the microfluidic cards were subject to quantile normalization before further analysis.

Author Contributions RDM, KC-B, and JPPM designed the study and wrote the manuscript; RDM and KC-B performed the experiments, YL analyzed the bioinformatics data, EV flow-sorted the different cell populations, KP-O and FJTS collected and provided the human thymocytes, and TW and AWL performed V-D-J rearrangement experiments. JPPM and RP supervised the study. All authors read and approved the paper, and declare no competing financial interests.

Acknowledgements We thank Tom Taghon for critically reviewing the manuscript. This study was supported by the Childhood Cancer Foundation (Stichting Kinderen Kankervrij, KiKa) grants KiKa2008-29, KiKa2013-116, KiKa2014-141 (RDM and KC-B), KiKa2011-82 (YL), the Dutch Cancer Foundation KWF2010-4691 (EV), and ZonMW E-RARE (grant no.40-41900-98-020 (KPO and FJTS).

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Cohen, P. Brennan, M. Brenner, F. Kim, T. Nageswara Rao, A. Wagers, T. Heng, J. Ericson, K. Rothamel, A. Ortiz-Lopez, D. Mathis, C. Benoist, N.A. Bezman, J.C. Sun, G. Min-Oo, C.C. Kim, L.L. Lanier, J. Miller, B. Brown, M. Merad, E.L. Gautier, C. Jakubzick, G.J. Randolph, P. Monach, D.A. Blair, M.L. Dustin, S.A. Shinton, R.R. Hardy, D. Laidlaw, J. Collins, R. Gazit, D.J. Rossi, N. Malhotra, K. Sylvia, J. Kang, T. Kreslavsky, A. Fletcher, K. Elpek, A. Bellemare-Pelletier, D. Malhotra, and S. Turley. 2013. The transcriptional landscape of alphabeta T cell differentiation. Nat Immunol 14:619-632. Pike-Overzet, K., D. de Ridder, F. Weerkamp, M.R. Baert, M.M. Verstegen, M.H. Brugman, S.J. Howe, M.J. Reinders, A.J. Thrasher, G. Wagemaker, J.J. van Dongen, and F.J. Staal. 2007. Ectopic retroviral expression of LMO2, but not IL2Rgamma, blocks human T-cell development from CD34+ cells: implications for leukemogenesis in gene therapy. Leukemia 21:754-763. Plum, J., M. De Smedt, M.P. Defresne, G. Leclercq, and B. Vandekerckhove. 1994. Human CD34+ fetal liver stem cells differentiate to T cells in a mouse thymic microenvironment. Blood 84:1587-1593. Radtke, F., A. Wilson, and H.R. MacDonald. 2004. Notch signaling in T- and B-cell development. Curr Opin Immunol 16:174-179. Res, P., E. Martinez-Caceres, A. Cristina Jaleco, F. Staal, E. Noteboom, K. Weijer, and H. Spits. 1996. CD34+CD38dim cells in the human thymus can differentiate into T, natural killer, and dendritic cells but are distinct from pluripotent stem cells. Blood 87:5196-5206. Rothenberg, E.V., J.E. Moore, and M.A. Yui. 2008. Launching the T-cell-lineage developmental programme. Nat Rev Immunol 8:9-21. Schmitt, T.M., and J.C. Zuniga-Pflucker. 2002. Induction of T cell development from hematopoietic progenitor cells by delta-like-1 in vitro. Immunity 17:749-756. Spits, H. 2002. Development of alphabeta T cells in the human thymus. Nat Rev Immunol 2:760- 772. Subramanian, A., P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, and J.P. Mesirov. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545-15550. Van Coppernolle, S., G. Verstichel, F. Timmermans, I. Velghe, D. Vermijlen, M. De Smedt, G. Leclercq, J. Plum, T. Taghon, B. Vandekerckhove, and T. Kerre. 2009. Functionally mature CD4 and CD8 TCRalphabeta cells are generated in OP9-DL1 cultures from human CD34+ hematopoietic cells. J Immunol 183:4859-4870. van Dongen, J.J., A.W. Langerak, M. Bruggemann, P.A. Evans, M. Hummel, F.L. Lavender, E. Delabesse, F. Davi, E. Schuuring, R. Garcia-Sanz, J.H. van Krieken, J. Droese, D. Gonzalez, C. Bastard, H.E. White, M. Spaargaren, M. Gonzalez, A. Parreira, J.L. Smith, G.J. Morgan, M. Kneba, and E.A. Macintyre. 2003. Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936. Leukemia 17:2257-2317. Vivinus-Nebot, M., P. Rousselle, J.P. Breittmayer, C. Cenciarini, S. Berrih-Aknin, S. Spong, P. Nokelainen, F. Cottrez, M.P. Marinkovich, and A. Bernard. 2004. Mature human thymocytes migrate on laminin-5 with activation of metalloproteinase-14 and cleavage of CD44. J Immunol 172:1397-1406. Weerkamp, F., M.R. Baert, M.H. Brugman, W.A. Dik, E.F. de Haas, T.P. Visser, C.J. de Groot, G. Wagemaker, J.J. van Dongen, and F.J. Staal. 2006. Human thymus contains multipotent progenitors with T/B lymphoid, myeloid, and erythroid lineage potential. Blood 107:3131-3137.

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Figure Legends

Figure 1. Consecutive stages of human early in vitro T-cell differentiation represent two major gene signatures. (A) Schematic representation of the OP9-DL1 co-culture with the sorting strategy of consecutive T-cell differentiation stages. The table displays the sorted populations (and the replicates) based on surface markers (CD45, CD34, CD7, CD5, CD1a, CD4, CD8) and number of days in co-culture. All populations were also gated for CD45+GFP- to exclude OP9- DL1 cells that are GFP positive. (B) Principle component analysis of 29 samples based on the

2179 probesets with high variance (top 5%) and log2 expression values (>8). These probesets were summarized into 1387 genes by taking the median of all probesets across a gene. (C) Hierarchical clustering analysis using 1387 genes. Pearson dissimilarity measure and average linkage were applied to cluster the samples, and Euclidean distance and Ward’s method were applied to define 16 distinct gene signatures with similar expression patterns. (D) Average expression pattern of genes in the 16 gene signatures. z-scores are on the y-axis and 29 populations on the x-axis, ordered according to Fig. 1C. The grey area represents 1 SD. (E) Gene set enrichment analysis (GSEA) on the in vitro T-cell differentiation dataset to select gene signatures that are significantly enriched in the early or late T-cell program (marked in green and bold). The Normalized Enrichment Score (NES) and False Discovery Rate (FDR) q-value for each gene signature are given on top of each sub-figure. Populations belonging to the early/late T-cell program were determined according to Fig. 1C. Gene sets with FDR q≤0.25 were considered significantly correlated to the class distinction.(Subramanian et al., 2005)

Figure 2. The in vitro gene expression signatures recapitulate in vivo signatures of pre- and post- T-cell committed thymocytes. (A) Hierarchical clustering analysis (Pearson average method) of the different sorted populations based on the 547 genes that discriminate early (characterized by gene signatures #7 and #8) from late (characterized by gene signatures #15, #16 and #2) T-cell programs. The clustering obtained is similar to Fig.1C where 1387 genes were used. (B) Gene set enrichment analysis of our in vitro gene signature on the gene expression dataset of consecutive T-cell development populations from murine thymi. The first and second sub-figures from the left are the analysis results using gene signatures #7 and #8. The Normalized Enrichment Score (NES), Nominal (NOM) p-value, and False Discovery Rate (FDR) q-value indicate that these two gene signatures correlate with the pre-commitment stages. Also shown in the first sub-figure is the list of top 10 input genes with the highest signal-to-noise ratios in this dataset. The third and fourth sub-figures are the same analysis for gene signatures #2, #15, and

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#16, which correlate with the post-commitment stages. (C) Hierarchical clustering (Euclidean distance and Ward’s method) on human thymocyte populations using 399 out of the 547 in vitro pre-/post-commitment signature genes (the remaining 148 genes were not profiled because they were not on the HG-U133A array used in the dataset by Dik et al., 2005). The dendrogram on the left shows the clustering of different sorted human thymocytes, separating the pre- and post- committed populations.

Figure 3. Loss of CD44 surface expression marks T-cell commitment in early human thymocytes. (A) Sorting strategy for isolating T-cell differentiation stages A-F from duplicate (n=2) 12- and 18-day co-cultures started with pooled CD34+ HSCs, based on surface markers CD45, CD34, CD7, CD5, CD1a, and CD44. (B) Hierarchical clustering (Pearson average) of sorted populations A-F using 47 selected T-cell development genes. Expression values were obtained by Taqman array microfluidic cards, followed by ANOVA to remove day and pool effects. (C) Sorting strategy for isolating T-cell developmental stages I-III from four independent donors (n=4), based on CD45, CD7, CD5, CD1a, and CD44 after pre-depletion of CD3-, CD4-, CD8-, and CD19-expressing thymocytes. (D) Hierarchical clustering (Pearson average) of sorted populations I-III using 47 selected T-cell development genes. Expression values were obtained by Taqman array microfluidic cards, followed by quantile normalization.

Figure 4. Loss of CD44 functionally marks human T-cell commitment. (A) GeneScan visualization of in-frame Dβ-Jβ (left column) and Vβ-Jβ (middle and right columns) gene rearrangements, determined using the BIOMED-2 multiplex PCR.(van Dongen et al., 2003) One representative example of three independent donors is shown. Healthy peripheral blood mononuclear cells were used as a positive, polyclonal control (top row); populations I-III are indicated (rows 2-4). Primers for the Jβ1 cluster were hexachloro-6-carboxy-fluorescein (HEX)- labeled (blue traces), and primers for the Jβ2 cluster were 6-carboxy-fluorescein (FAM)-labeled (green traces). Red traces: internal size markers. (B) Flow cytometry analysis of sorted human thymocyte populations I-III after 10 days of co-culture on OP9-DL1:GFP mixed cells. Differentiation into various hematopoietic lineages is defined as follows: T-lineage (blue): CD5+CD1a+, NK-lineage (red): CD56+CD94+, B-lineage (yellow): CD19+, myeloid lineage (green): CD33+. During differentiation, all populations remain CD45+ and CD7+. (C) Absolute cell numbers ±SD of each differentiated and proliferated hematopoietic lineage from a representative of three experiments, after 10 days of co-culture on a layer of OP9-DL1, a 1:1 mix, or OP9-GFP

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(ctrl.) cells. The number of cells from populations I-III used to initiate the co-cultures on day 0 was 4,000 cells/well, indicated by the dashed horizontal line.

Figure 5. Schematic comparison of human and mouse early T-cell development in the thymus. Bone marrow-derived lymphoid progenitors enter the thymus (depicted as a blue oval) and exhibit dim CD44 expression. T-cell commitment is marked by the downregulation of CD44. As cells mature, they regain CD44 expression.

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Chapter 6

T-Cell Acute Lymphoblastic Leukemia Oncogenes hijack T-Cell Developmental Programs for Leukemogenesis: the arrest of Early T-cell programs by MEF2C, LYL1 or LMO2

Rui D. Mendes1, Kirsten Canté-Barrett1, Yunlei Li1, Wilco Smits1, Rob Pieters1,2 and Jules P.P. Meijerink1

1Department of Pediatric Oncology/Hematology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam, The Netherlands; 2Princess Máxima Center of Pediatric Oncology, Utrecht, The Netherlands.

Manuscript in preparation

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Abstract Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is a cancer of developing T-cells in the thymus. The T-ALL subtypes are characterized by specific chromosomal rearrangements driving the ectopic expression of oncogenes that result in arrest of leukemic cells at specific T-cell developmental stages. Here, we show that T-ALL subtypes are strongly associated with the expression of particular T-cell developmental programs. Immature/early T-cell precursor (ETP)- ALL and TLX subtypes of T-ALL express a pre-commitment program whereas the proliferative and TALLMO subtypes express a post-commitment program. This suggests that T-ALL oncogenes require the expression of particular T-cell transcriptional programs to exert an oncogenic function, and therefore may hijack T-cell developmental programs by facilitating a developmental arrest. That is further supported by functional in vitro studies where we investigate the impact of typical ETP-ALL (proto-)oncogenes, including MEF2C, LYL1 or LMO2 on early T-cell development. Umbilical cord blood stem cells transduced with lentiviral expression constructs that constitutively drive MEF2C, LYL1 or LMO2 were cultured on OP9- DL1 cells for differentiation towards the T-cell lineage. Expression of MEF2C or its putative cofactors (LYL1 or LMO2) blocks T-cell development at ETP-stages, in line with their roles in immature T-ALL. Contrarily, expression of the oncogenes in T-cells beyond the ETP-stages does not lead to T-cell development arrest, indicating that their leukemogenic potential is dependent on ETP-stage and expression of pre-commitment transcriptional networks. Thus, we conclude that the specific interaction of an aberrant oncogenic transcription factor with specific transcriptional network in a developing T-cell is an important requirement for oncogenesis.

Introduction T-cell development in the thymus is a complex process that depends on sequential transcriptional and epigenetic events that promote T-cell lineage commitment while simultaneously suppressing alternative cell fates.[1, 2] During this process, inappropriate activation of specific oncogenic transcription factors arrest thymocytes at specific T-cell developmental stages.[3] The ectopic expression of these oncogenes results from specific chromosomal rearrangements that place T-ALL oncogenes under the control of regulatory elements, such as enhancers of T-cell receptor or BCL11B genes, promoters from other genes, or occasionally result in oncogenic fusion proteins. Based on unsupervised gene cluster analysis we previously identified four T-ALL subtypes with specific gene expression signatures that are denoted as immature, TLX, proliferative or TALLMO clusters.[4] 114

Gene expression signature and clustering analysis of the immature T-ALL cluster highly overlap with the previously described high-risk early T-cell precursor (ETP)-ALL,[5] and represent the same disease entity.[6] The immature/ETP-ALL subtype is characterized by the overexpression of stem cell associated transcription factors that are normally extinguished during early T-cell development. These include the high expression of the MEF2C transcription factor,[4] and the expression of LMO2 and LYL1 [7, 8] that form a macromolecular transcriptional complex.[9] We have previously demonstrated that the immature/ETP-ALL subtype exhibits rearrangements of MEF2C and translocations involving transcription factors that target MEF2C (e.g. NKX2.5, SPI1) or MEF2C-associated cofactors (e.g. NCOA2),[4] and we showed that MEF2C regulates transcriptions factors as LYL1, LMO2 and HHEX.[4] This implies that those genes may be involved in MEF2C-deregulated T-ALL. The ectopic expression of LMO2 and HHEX are associated with a hematopoietic stem cell (HSC)-specific transcriptional program that is responsible for an arrest and self-renewal of preleukemic thymocytes, which impedes the new entry of seeding T-cell precursors in the thymus.[10, 11] Recently, this disruption in the turnover of cells in thymus was described to promote T-ALL.[12] However, these transcription factors are expressed in normal T-cell precursors that continuously seed the thymus, demonstrating that their expression is not exclusive for T-ALL.[13, 14] Normally, these transcription factors can exert roles in proliferation, survival and self-renewal of hematopoietic cells, and their expression is retained at the early phases of T-cell development until T-cell lineage commitment. [13] (R.D. Mendes and K. Cante-Barrett et al. unpublished data) In our previous study, we used the OP9-DL1 in vitro culture system[15] to investigate the transcriptional landscape of differentiating human T-cells from umbilical cord-blood isolated HSCs. (Chapter 5, R.D. Mendes and K. Cante-Barrett et al. unpublished data) The changes in gene expression of five subsequent development stages revealed important early and late T-cell programs that are conserved in mouse and human T-cell development in vivo, and represent pre-commitment and post-commitment programs, respectively. Importantly, we found that loss of CD44 functionally defines human T-cell lineage commitment in ETP cells, before the acquisition of CD1a surface expression. In the present study, we have investigated the effect of ectopic expression of MEF2C, LYL1 or LMO2 transcription factors in T-cell development to better understand how aberrant expression of these (proto-)oncogenes can contribute to differentiation arrest during early T-cell development. Thus, we ectopically expressed MEF2C, LYL1 or LMO2 in HSCs and then cultured these cells in the OP9-DL1 in vitro system. These proteins each result in the

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developmental arrest within the ETP-stage and may therefore play an important role in the pathogenesis of ETP-ALL.

Materials and methods

Patients and gene expression profiles A total of 117 primary pediatric T-ALL patient samples enrolled in the Dutch Childhood Oncology Group (DCOG) protocols or the German Cooperative Study Group for Childhood Acute Lymphoblastic Leukemia study (COALL) were included in this study. Informed consents were in accordance with the institutional review boards of the Erasmus MC, Rotterdam, and the Declaration of Helsinki. The microarray datasets are available at http://www.ncbi.nlm.nih.gov/geo/ under accession number GSE26713.

Lentiviral expression vectors The third-generation self-inactivating lentiviral LeGO-iC2 vector[16] was adapted into a Gateway compatible destination vector (LeGO-DEST) by replacing the ApaI/PciI 2kb insert with the Gateway ccdB cassette (Life Technologies, Bleiswijk, the Netherlands). The lentiviral expression constructs were assembled by cloning the Spleen Focus Forming Virus (SFFV) promoter, the MEF2C, LYL1 or LMO2 oncogenes flanked by a Thosea asigna virus 2A (T2A) element [17] and the blue fluorescent protein reporter (mTagBFP). These components in combination with other lentiviral elements are flanked by long terminal repeats, encoding for the proviral RNA. The 3 lentiviral expression constructs encoding the oncogenes LMO2, LYL1 or MEF2C exhibit proviral RNA lengths of 4830 bp, 5190 bp and 5750 bp, respectively. The control vector is solely composed of the SFFV promoter followed by the BFP reporter and contains 4215 bp.

Virus Production Virus productions were performed according to the protocol described in Chapter 4. Briefly, the different virus batches were produced in serum-free Opti-MEM I with Glutamax (Life Technologies) and were concentrated using VIVASPIN 20 centrifugation concentration columns (Sartorius, Goettingen, Germany). The number of viral particles was measured by quantification of proviral RNA copies using RT-qPCR.

Western-blot

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Lentiviral vectors were functionally tested by western-blot (WB), using transduced Jurkat and Loucy T-ALL cell lines. Transduced cells were propagated and monitored for the expression of BFP protein. Cells were lysed in RIPA buffer and 40 µg of total protein was loaded in lanes of Any kD Mini-PROTEAN TGX Precast Protein Gels (Biorad, Veenendaal, the Netherlands). The primary antibodies (abs) with reactivity against the human proteins were used according to the manufacturer’s protocol; and include the anti-2A Peptide (ABS31) polyclonal Rabbit Ab (Millipore BV, Amsterdam-Zuidoost, the Netherlands), the anti-LMO2 polyclonal Goat Ab (R&D systems), the anti-LYL1 polyclonal Rabbit Ab (Thermo Fisher Scientific, Waltham, MA, USA), and the anti- MEF2C (D80C1) XP monoclonal Rabbit Ab (Cell Signaling, Danvers, MA, USA). Incubations with the primary antibodies were followed by incubations with secondary IRDye 680RD goat anti-rabbit or 680LT donkey anti-goat antibodies (LI-COR Biosciences, Lincoln, NE, USA), and detected with the Odyssey imaging system (LI-COR).

Human umbilical cord blood Human umbilical cord blood (UCB) samples were obtained by syringe extraction and collected in a blood-pack unit containing citrate phosphate dextrose anticoagulant from mothers after delivery in local hospitals. Mononuclear cells (MNCs) were isolated by Ficoll-Paque density centrifugation, washed and frozen in 10% dimethyl sulfoxide (DMSO) and 90% fetal bovine serum (FBS) for later use. Informed consents were in accordance with the Institutional Review Boards of the Erasmus MC (Rotterdam, the Netherlands) and the Declaration of Helsinki.

Isolation of CD34+cells from umbilical cord blood Frozen MNCs from UCB samples were thawed, washed and labeled with MicroBeads conjugated to the monoclonal mouse anti-human CD34 antibody according to the manufacturer’s protocol (Miltenyi Biotec, Bergisch Gladbach, Germany). Magnetic separation was performed twice using two MACS Columns (Miltenyi Biotec), consistently reaching a CD34+ purity of approximately 95%. CD34+ HSCs were resuspended to concentration of 0.5x106 cell/ml in pre-stimulation medium consisting of serum-free X-VIVO 10 medium (Lonza, Verviers, Belgium) supplemented with 50 ng/ml rhSCF (R&D systems, Abingdon, UK), 20 ng/ml rhTPO (R&D systems) and 50 ng/ml rhFlt3L (Miltenyi Biotec). Cells were incubated for 16-20 hours at

37ºC and 5% CO2.

Transduction of Human CD34+ cells

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Cells were transduced immediately after 16-20 hours of pre-stimulation. Transduction of cells was performed according to the protocol described in Chapter 4. Briefly, 96-well nontreated surface plates (BD Biosciences, Breda, The Netherlands) were coated with retronectin (Takara, Saint-Germain-en-Laye, France), and 1.0x105 cells were mixed with 109 -1010 virus particles in a final volume of 200 µl/well, followed by spinoculation. Transduced cells were incubated for 24 hours at 37ºC, 5%CO2.

OP9-DL1 co-cultures OP9-DL1 co-cultures were performed according to the original protocol.[18] Briefly, transduced HSC were plated into 6-well cell culture plates (Greiner Bio-One B.V., Alphen aan den Rijn, Netherlands), which were seeded the day before with 7x104 OP9-DL1 cells to reach 70-80% confluence. The α-minimal essential medium (α-MEM) (Life Technologies) used for culture was supplemented with 20% fetal bovine serum (FBS) (Integro b.v., Dieren, the Netherlands) plus 10 U/mL Penicillin, 10 µg/mL of streptomycin, and 0.025 µg/mL fungizone (PSF) (Life technologies). Recombinant human stem cell factor (SCF) (10 ng/mL) (R&D systems, Abingdon, UK), FLT3L (5 ng/mL) (Miltenyi Biotec), and interleukin-7 (IL7) (2 ng/mL) (Miltenyi Biotec) were added at the initiation of coculture and every 2-3 days during transfer of HSC-derived cells onto fresh plates that had been seeded with OP9-DL1 cells beforehand. Cocultures were disaggregated by vigorous pipetting and passaged through a 40-µm filter to reduce stromal cell line aggregates.

Flow-cytometry T-cell differentiation was followed-up by flow-cytometry using the MACS Quant (Miltenyi Biotec). The different stages of differentiation were based on the expression of cell surface markers including the following antibodies (with clone identification): CD1a (HI149), CD7 (M-T701), CD13 (WM15), CD33 (WM53), CD34 (8G12) (BD Biosciences), CD4 (VIT4), CD5 (UCHT2), CD8 (BW135/80), CD19 (LT19), CD33 (AC104.3E3), CD34 (AC136), CD44 (DB105), CD45 (5B1) (Miltenyi Biotec). The data analysis was performed using the FlowJo software (TreeStar Inc., Ashland, OR).

Results T-ALL subtypes are associated with pre-/post- commitment developmental programs In our previous study, we performed gene expression arrays of consecutive T-cell developmental stages from early thymus progenitors (ETP) to CD4+CD8+ double-positive (DP) T-cells (Supplemental Figure S1a). Using bioinformatics tools we obtained a gene signature of 118

547 genes, which is composed of differentially expressed genes across T-cell differentiation (Supplemental Figure S1b). Importantly, the changes in gene expression of the subsequent stages matched those of human and murine in vivo thymocyte subsets, and consequently two distinct T-cell differentiation programs named “early” and “late” were correlated with in vivo pre- and post- αβ T-cell commitment profiles, respectively (Supplemental Figure S1b). Following that work, here we examine whether this gene signature can distinguish T-ALL patient subtypes, since different T-ALL subtypes exhibit a specific developmental arrest that is driven by the ectopic expression of specific oncogenic rearrangements. Thus, the gene signature of 547 genes was applied to the gene expression data obtained from 117 pediatric T- ALL samples. Interestingly, the PCA mapping showed clustering of the 117 samples into four main clusters that closely resembled the four clusters as previously obtained by unsupervised cluster analysis,[4] i.e. the immature/ETP-ALL, TLX, proliferative and TALLMO clusters (Figure 1a). In addition, analysing T-ALL samples together with the T-cell sorted fractions obtained using the OP9-DL1 in vitro system, revealed that UCB-CD34+ stem cells co-cluster with immature/ETP-ALL samples. Furthermore, immature/ETP-ALL and TLX cases, which frequently exhibit an immature or γδ-lineage associated phenotype,[8] both express a pre-commitment gene signature as they co-cluster with “early”/pre-commitment T-cell stages. Proliferative and TALLMO cases express a post-committed gene signature and co-cluster with “late”/post- commitment T-cell stages (Figure 1b). Yet, a few samples are apparently misclustered (arrows), fact that can be possibly explained by the translocation partners observed on their rearrangements. For instance, translocations or fusions with Bcl11b, which is a haplo-insufficient tumor suppressor, could impair TALLMO rearranged leukemic cells to pass beyond the T-cell commitment point, whereas TLX3 translocations with TCR-β or TCR-δ loci among normal, unrearranged Bcl11b loci may allow cells to pass T-cell commitment and αβ-lineage development. (work in progress) Instead of the 547 genes signature, which is purely based on differentially expressed genes during normal T-cell development, PCA mapping was also performed using the 435 probeset (398 genes) gene signature (Figure 1c), which previously separated T-ALL patient samples into the 4 T-ALL clusters by using an unsupervised approach.[4] As expected, the samples could be separated into the 4 T-ALL clusters (immature/ETP-ALL,TLX, proliferative and the TALLMO), showing a close overlap with the clustering that distinguished pre- and post- commitment T-cell programs (Figure 1a). This suggests that the 398-gene signature could already contain differentially expressed T-cell developmental genes among the T-ALL clusters. We then examined the coinciding genes between the 547 T-cell development gene signature

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and the 398 gene set (435 probes) from our previous unsupervised T-ALL cluster analysis, and identified 107 overlapping genes that are differentially expressed during T-cell development as well as among the 4 clusters of T-ALL. Hierarchical clustering of the T-ALL samples based on these 107 genes revealed four main gene groups (I-IV) that are closely related to the four T-ALL clusters (Figure 1d and Supplemental Table S1). For instance, immature/ETP-ALL is associated with high expression of genes known to be expressed during the ETP-stage, such as LMO2 and HHEX (Group I); the TLX cluster that comprises HOXA-activated and TLX3-rearranged T-ALL cases expresses NOTCH3, a relevant gene for gamma-delta T cell differentiation (Group II)[19]; the proliferative cluster exhibits genes as DNTT (also known as TDT) that may reflect the initiation of T-cell receptor recombination events, and CD1A, which is a hallmark of this cluster (Group III), and finally the TALLMO group contained genes of more mature T-cells such as LEF1 and CD8 (Group IV). Thus, the gene signature of 435 probesets that we established to cluster different T-ALL samples is highly enriched with genes from the T-cell developmental signature (Supplemental Figure S1c), indicating that T-ALL oncogenes cooperate with T-cell developmental programs for leukemogenesis. This may imply that oncogenes can only exert their leukemogenic potential and promote differentiation arrest when ectopically expressed during specific T-cell developmental programs (Figure 1e).

Validation of lentiviral vectors expressing MEF2C, LYL1, or LMO2 in Jurkat cells. We further studied this in OP9-DL1 co-cultures analyzing the effect of various genes that are highly expressed in immature/ETP-ALL patients (LYL, LMO2 and MEF2C) on normal T-cell development. For this, we designed and produced lentiviral vectors that allow the overexpression of MEF2C, LYL1 or LMO2 in differentiating HSCs in vitro (Figure 2a). Following virus production and titration of the lentiviral oncogene vectors or BFP-control vector we checked expression of oncogenes upon transduction of T-ALL cell lines, Jurkat (Figure 2b) and Loucy cells (Supplemental Figure S2). The oncogenes and BFP moieties are efficiently expressed in target cells and therefore transduction efficiencies can be measured by the percentage of BFP- positive cells (Figure 2b, upper panel). The percentage of transduced Jurkat cells (BFP-positive) remained unchanged (>95%) for over three weeks of culture, reflecting stable integration of the viral genome in the host DNA (data not shown). The only exception occurred to cells transduced with the MEF2C lentiviral vector as the percentage of BFP-positive cells gradually decreased, possibly reflecting toxic effects of MEF2C overexpression. To confirm the ectopic expression of the oncogenes on transduced cells, MEF2C, LYL1 and LMO2 were identified by western-blot analysis using T2A or specific antibodies (Figure 2b and Supplemental Figure S2).

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Constitutive Expression of MEF2C, LYL1, or LMO2 impairs T-cell development To study the effect of MEF2C, LYL1 or LMO2 on normal T-cell development, we transduced umbilical cord blood-derived HSCs with each lentiviral construct, in at least 3 independent experiments. All lentiviral batches transduced UCB-HSCs with 40-60% transduction efficiencies, as measured by the percentage of BFP-positive cells (data not shown). The MEF2C construct provided low transduction efficiency that barely exceeded 10% transduction rates. The effect of constitutive expression of each of these genes in differentiating HSCs was analyzed upon culture of transduced HSCs on the OP9-DL1 in vitro system (Figure 3a). The dynamics of the co-cultures was evaluated by detection of CD34 and CD7 expression (Figure 3b and Figure 3c). HSCs are CD34+CD7-, CD34-CD7- are non T-cell populations (including myeloid-, B- and NK-cells), and CD34+CD7+ and CD34-CD7+ cells represent T-cell populations (Figure 3b). The BFP reporter allows for flow-cytometry gating and analysis of transduced HSCs (Figure 3c, two right graphs with blue dots), whereas BFP-negative cells from non-transduced HSCs can be monitored as a control in the same culture (two left graphs with pink dots). BFP-negative cells from the transduction experiments with each expression construct and the BFP-positive cells transduced with the BFP control vector normally differentiate into the T-cell lineage (top graphs). However, expression of each of the three ETP genes LMO2, LYL1 or MEF2C results in a reduced number of T-cells. This was not due to a defective T-cell development caused by a culture defect, as untransduced HSCs (BFP-) in the same experiment efficiently generated T-cells. MEF2C not only blocked T-cell development, but also blocked differentiation of HSCs into alternative B-, myeloid or NK- lineages. (Figure 3c) Also, expression of LYL1 or LMO2 reduced the T-cell differentiation potential of HSCs, while the potential to develop into alternative cell fates remains unchanged. As a consequence, the number of untransduced (BFP-) T-cells and other cell lineages is higher compared to the BFP control due to the extra availability of space and cytokines in the absence of surviving transduced cells. Notably, ectopic expression of MEF2C, LYL1 or LMO2 in HSCs arrested developing cells at early T-cell development (Figure 3d). The expression of MEF2C leads to an immediate block in T-cell differentiation and cells fail to acquire CD7 as the first T-cell marker (Day 5). Instead, with increased passage cells gradually lose CD34 expression and eventually disappear from the co-culture due to apoptosis. Overexpression of LYL1 or LMO2 allows the cells to acquire CD7, although fewer BFP+, CD7+ cells are obtained compared to the control (Day 9 and 12, respectively), and there is also a lower percentage of CD7+ cells losing CD34 as a parameter of

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T-cell maturation. Thus, constitutive expression of MEF2C, LYL1, or LMO2 impairs early T-cell development in vitro, comparable to what is seen in ETP-ALL patients.

Constitutive expression of MEF2C, LYL1, or LMO2 is dependent on ETP-transcriptional programs to promote differentiation block before T-cell commitment To distinguish and characterize the differentiation arrest caused by each oncogene, we measured five immunophenotype markers (CD34, CD44, CD7, CD5, CD1a) that can discriminate consecutive T-cell developmental stages (A-F), from HSC till the CD1+ stage (Figure 4a). Those populations together with the non T-cell (M) population represent >85% of the total of cells detected in each well by flow-cytometry, while <15% of cells remain un- characterized as these fall outside the defined flow-cytometry gates. The T-cell lineage commitment occurs in the CD7+CD5+ stage when cells lose the expression of the CD44 surface marker (Chapter 5). Figure 4b shows the total number of BFP-positive cells per fraction, representing the emergence of consecutive T-cell developmental stages over different days of co-culture. Cells transduced with the BFP control vector at day 2 of co-culture remain mostly as hematopoietic progenitors (CD34+CD44+CD7-), while at days 5 and 8 a substantial number of early T-cells are observed based on the expression of CD7 and CD5 (fractions B and C, respectively). Besides the presence of early T-cell populations, day 12 is characterized by further differentiated T-cell subsets that have lost CD34 expression (D-E2), including cells that have undergone T-cell commitment (E2) as characterized by loss of CD44. Populations expressing CD1a appear at day 18 of co-culture. Importantly, the dynamics of the populations (A-F) during the co-culture is identical between HSCs that are transduced with the BFP control vector (BFP-positive) in comparison to cells that have not been transduced (BFP-negative) (Supplemental Figure S3a). The expression of MEF2C leads to an immediate differentiation block of the transduced (BFP+) HSCs, as practically there are no cells that have differentiated in either the T-cell or non- T-cell lineages (Figure 4b). The expression of LYL1 facilitates a partial differentiation block at the most immature CD7+ ETP stage that impairs further differentiation into CD7+CD5+ cells (fraction C). Ectopic expression of LMO2 facilitates T-cell development up till the CD7+CD5+ ETP-stage, where cells are partially blocked. Few cells may escape this block and further lose CD34 and CD44, and acquire CD1a (D-F). Overall, each of these oncogenes facilitate a T-cell developmental arrest of differentiating T-cells before the T-cell commitment checkpoint, consistent with the early T-cell development block of leukemic ETP-ALL cells (Figure 4c). This indicates that the oncogenes may only exert their ability to block differentiation in the context of

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appropriate T-cell development. In order to prove this point, we investigated whether MEF2C, LYL1, or LMO2 might block T-cell differentiation in further differentiated T-cells. We therefore transduced early CD7+ T-cells, and studied their differentiation potential on OP9-DL1 stromal cells. Remarkably, when CD7+-sorted cells were transduced (day 6 of co-culture) with the MEF2C or LYL1 lentiviral vector, those cells were able to differentiate normally into the various T-cell stages (Supplemental Figure S3b). This indicates that the differentiation block caused by overexpression of MEF2C or LYL1 is dependent on the presence of a very premature T-cell developmental program, before the expression of CD7. In contrast, sorted CD7+ cells transduced with LMO2 lentiviral vector still exhibited a partial block at the CD7+CD5+ ETP-stage (Supplemental Figure S3b). Overall, constitutive expression of MEF2C, LYL1 or LMO2 in vitro results in a premature differentiation arrest of developing T-cells, but at different ETP-stages. Further, we also demonstrate that MEF2C, LYL1 or LMO2 are dependent on those early T-cell developmental programs to exert their leukemogenic effect.

Discussion In this study we reveal that T-cell development gene signatures provide a similar rearrangement- associated clustering of T-ALL subtypes as previously obtained by unsupervised cluster analysis. We further demonstrate that T-ALL oncogenes are only able to exert an oncogenic function in the context of expression of particular T-cell transcriptional programs. In addition, the observation that the previously described T-ALL signature was highly enriched for important T- cell development regulators indicated that the T-ALL oncogenes highly cooperate with T-cell developmental programs for leukemogenesis. This was further supported by the OP9-DL1 in vitro studies, where MEF2C, LYL1 or LMO2 were dependent on the transcriptional networks from the initial stages of T-cell development to induce a differentiation arrest characteristic of ETP-ALL. In addition, we showed that the TLX subtype that is mostly associated with γδ-lineage express a pre-commitment transcriptional program of T-cell development. This is in line with our own patient data, as most TLX3-rearranged cases have translocations where BCL11B is involved, and are therefore haploinsufficient for the essential role of BCL11B in T-cell lineage commitment and maintenance of T-cell identity.[20, 21] Of note, the OP9-DL1 system clearly favors the differentiation of αβ-T cells, thus to specifically comprehend the transcriptional program of γδ-lineage a similar in vitro system needs to be established that will support γδ-T-cell lineage development. The transcription factors MEF2C, LYL1 and LMO2 play central roles in normal early T- cell development and consequently abnormal regulation of MEF2C can promote 123

leukemogenesis, resulting in ETP-ALL. We have previously demonstrated that LYL1 and LMO2 are regulated by MEF2C, and showed that MEF2C can directly bind to the promoter regions of LMO2. These observations support the oncogenic roles of LYL1 and LMO2 in MEF2C- deregulated ETP-ALL.[4] Thus, it is crucial to understand both the oncogenic function of each protein individually and their involvement in major transcriptional complexes by identification of protein-protein interactions. ETP-ALL cells usually express myeloid surface markers (CD13 and/or CD33) and have lower expression of T-cell specific genes compared to other types of T- ALL.[22] This may be derived from the fact that the expression of MEF2C is higher in myeloid and B-cells compared to T-cells and may therefore favor the differentiation of other non T-cell lineages, and also ectopic expression of MEF2C has been linked to MLL-rearranged acute myeloid leukemia.[14] Consequently, MEF2C-transduced HSCs in the OP9-DL1 in vitro system blocks T-cell development at very early stage when cells are still CD34+, and exhibit high expression of CD13 and CD33 in cells that attempt to differentiate into non T-cell lineages (such as B-, myeloid or NK-cells). Possibly, T-cells arrested at this immature/ETP-ALL stage do not survive in vitro, as these cells may not be sustained with enough proliferation and survival signals, which may be supported by IL7R[23] or RAS mutations in ETP-ALL. (Y. Li and J.G. Buijs-Gladdines et al. unpublished data). Importantly, the MEF2C-driven oncogenic mechanism that leads to differentiation block in ETP-cells is dependent on a very early T-cell developmental stage, as MEF2C overexpression at later stages did not provoke developmental arrest in vitro. Furthermore, constitutive expression of LYL1 or LMO2 in HSC is also incompatible with development into and beyond the T-cell commitment checkpoint and these proteins could therefore play important roles in the pathogenesis of ETP-ALL. The partial block observed at the constitutive expression of LYL1 or LMO2 may be related to “escaper cells” that have a lower expression of those oncogenes or due to continuous NOTCH1-activation promoted by the DL1- ligand expressed on the membrane of OP9-DL1 cells that enforces T-cell differentiation. Similarly, a study using fetal thymus organ cultures showed that human CD34+ cells with ectopic expression of LMO2 result in an incomplete developmental block, exhibiting a lower percentage of CD1+ cells compared to untransduced cells.[24] However, LMO2 is an oncogene for TALLMO patients that have arrested at more advanced αβ-lineage T-cells, therefore ectopic expression of LMO2 may facilitate multiple stages of T-cell developmental arrest. In line with this, a recent study with humanized NSG mice transplanted with human CD34+ stem/progenitor cells that were genetically modified to overexpress LMO2, caused 3 aberrant effects on human T-cell development in vivo.[25] Specifically, it was observed a block at the DN/ ISP stage, an accumulation of CD4+CD8+ DP CD3- cells, and an altered CD8/CD4 ratio with enhanced

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peripheral T lymphocytes. These results highlight the limitations of the OP9-DL1 in vitro system in developing T-cells into more mature stages, and demonstrate the strength of complementing this kind of studies with humanized mouse model. Furthermore, CD2-LMO2 mice give rise to highly penetrant T-ALL by two distinct patterns of gene expression: with ETP-ALL features such as high expression of HHEX, LYL1 and MEF2C [10] or alternatively, with Notch1 target gene activation. [26] These mice studies point to a synergy between co-expression of LMO2, LYL1 or HHEX with ectopic expression of MEF2C in ETP-ALL. For instance, HHEX and LYL1 seem to tightly collaborate with MEF2C as the incidence of T-ALLs in those mice is strongly reduced in the absence of each transcription factor. [9, 26] Therefore, we hypothesize that MEF2C synergizes with LMO2 and LYL1 in the dysregulation of important signaling pathways, resulting in ETP-ALL. Overall, we conclude that T-ALL oncogenes can only promote leukemogenesis if they are activated at specific developmental stages where they can perturb normal T-cell development. This hypothesis is supported by cytogenetic analysis of T-ALL patients, as specific translocations (e.g. Bcl11b or TCR rearrangements) only occur at developmental contexts when the chromatin of those loci is open. The developmental arrest allows the rapid acquisition of additional genetic mutations, so called type-B mutations that will give proliferative advantage to those pre-leukemic cells and ultimately lead to T-ALL. The in vitro system approach is a valuable tool to investigate the mechanisms that promote cellular arrest and it may help to understand how specific oncogenes and genetic lesions cooperate during T-cell leukemogenesis and disease progression.

Acknowledgements R.D.M. and K.C.-B. were financed by the Children Cancer Free Foundation (Stichting Kinderen Kankervrij (KiKa 2007-12, KiKa 2008-29 and KiKa 2013-116)).

Contribution R.D.M., K.C.-B. and J.P.P.M. designed the study; R.D.M., K.C.-B. and W.K.S. cloned the lentivirus expression constructs and produced the lentiviral constructs. R.D.M. performed the OP9-DL1 experiments and wrote the manuscript. R.D.M., K.C.-B., R.P. and J.P.P.M. analyzed and interpreted data; and all authors read, revised, and approved the paper.

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References 1. Rothenberg, E.V., J.E. Moore, and M.A. Yui, Launching the T-cell-lineage developmental programme. Nat Rev Immunol, 2008. 8(1): p. 9-21. 2. Zhang, J.A., et al., Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity. Cell, 2012. 149(2): p. 467-82. 3. Meijerink, J.P., Genetic rearrangements in relation to immunophenotype and outcome in T-cell acute lymphoblastic leukaemia. Best Pract Res Clin Haematol, 2010. 23(3): p. 307- 18. 4. Homminga, I., et al., Integrated transcript and genome analyses reveal NKX2-1 and MEF2C as potential oncogenes in T cell acute lymphoblastic leukemia. Cancer Cell, 2011. 19(4): p. 484-97. 5. Coustan-Smith, E., et al., Early T-cell precursor leukaemia: a subtype of very high-risk acute lymphoblastic leukaemia. Lancet Oncol, 2009. 10(2): p. 147-56. 6. Zuurbier, L., et al., Immature/early T-cell precursor acute lymphoblastic leukemia patients treated on the COALL-97 protocol are not associated with poor outcome Submitted for publication, 2012. 7. Ferrando, A.A., et al., Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell, 2002. 1(1): p. 75-87. 8. van Grotel, M., et al., Prognostic significance of molecular-cytogenetic abnormalities in pediatric T-ALL is not explained by immunophenotypic differences. Leukemia, 2008. 22(1): p. 124-31. 9. McCormack, M.P., et al., Requirement for Lyl1 in a model of Lmo2-driven early T-cell precursor ALL. Blood, 2013. 122(12): p. 2093-103. 10. McCormack, M.P., et al., The Lmo2 oncogene initiates leukemia in mice by inducing thymocyte self-renewal. Science, 2010. 327(5967): p. 879-83. 11. Shields, B.J., et al., Hhex regulates Kit to promote radioresistance of self-renewing thymocytes in Lmo2-transgenic mice. Leukemia, 2015. 29(4): p. 927-38. 12. Martins, V.C., et al., Cell competition is a tumour suppressor mechanism in the thymus. Nature, 2014. 509(7501): p. 465-70. 13. Yui, M.A. and E.V. Rothenberg, Developmental gene networks: a triathlon on the course to T cell identity. Nat Rev Immunol, 2014. 14(8): p. 529-45. 14. Cante-Barrett, K., R. Pieters, and J.P. Meijerink, Myocyte enhancer factor 2C in hematopoiesis and leukemia. Oncogene, 2014. 33(4): p. 403-10. 15. Schmitt, T.M. and J.C. Zuniga-Pflucker, Induction of T cell development from hematopoietic progenitor cells by delta-like-1 in vitro. Immunity, 2002. 17(6): p. 749-56. 16. Weber, K., et al., A multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors for functional gene analysis. Mol Ther, 2008. 16(4): p. 698-706. 17. Szymczak, A.L. and D.A. Vignali, Development of 2A peptide-based strategies in the design of multicistronic vectors. Expert Opin Biol Ther, 2005. 5(5): p. 627-38. 18. Holmes, R. and J.C. Zuniga-Pflucker, The OP9-DL1 system: generation of T- lymphocytes from embryonic or hematopoietic stem cells in vitro. Cold Spring Harb Protoc, 2009. 2009(2): p. pdb prot5156. 19. Van de Walle, I., et al., Specific Notch receptor-ligand interactions control human TCR- alphabeta/gammadelta development by inducing differential Notch signal strength. J Exp Med, 2013. 210(4): p. 683-97. 20. Li, L., M. Leid, and E.V. Rothenberg, An early T cell lineage commitment checkpoint dependent on the transcription factor Bcl11b. Science, 2010. 329(5987): p. 89-93. 21. Li, P., et al., Reprogramming of T cells to natural killer-like cells upon Bcl11b deletion. Science, 2010. 329(5987): p. 85-9.

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22. Zhang, J., et al., The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature, 2012. 481(7380): p. 157-63. 23. Zenatti, P.P., et al., Oncogenic IL7R gain-of-function mutations in childhood T-cell acute lymphoblastic leukemia. Nat Genet, 2011. 43(10): p. 932-9. 24. Pike-Overzet, K., et al., Ectopic retroviral expression of LMO2, but not IL2Rgamma, blocks human T-cell development from CD34+ cells: implications for leukemogenesis in gene therapy. Leukemia, 2007. 21(4): p. 754-63. 25. Wiekmeijer, A.S., et al., Overexpression of LMO2 causes aberrant human T-Cell development in vivo by three potentially distinct cellular mechanisms. Exp Hematol, 2016. 44(9): p. 838-849.e9. 26. Smith, S., et al., LIM domain only-2 (LMO2) induces T-cell leukemia by two distinct pathways. PLoS One, 2014. 9(1): p. e85883.

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Figure legends

Figure 1. T-ALL subtypes are associated with pre-/post- commitment developmental programs (a) Co-clustering of 117 T-ALL patient samples and in vitro T-cell sorted fractions (Chapter 5). The PCA analysis is based on the 547 genes that are differentially expressed across T-cell differentiation. The T-ALL subtypes are divided into the four T-ALL unsupervised clusters: Immature/ETP-ALL, TLX, Proliferative and TALLMO; and the T-cell sorted fractions are divided into CD34+ UCB-derived stem/progenitor cells, pre-commitment and post-commitment stages. (b) Hierarchical clustering analysis using the Kendall average method for 236 genes (547- Var50Val50). The dendogram at the left represents the similarity between the 117 T-ALL patients divided into 4 T-ALL clusters. The dendogram at the top indicates how related is the expression of the 547 genes throughout the different T-ALL samples. The dashed line represents the T-cell lineage commitment and the two arrows identify two T-ALL samples that are misclustered due to translocation partners (c) PCA mapping of 117 T-ALL patient samples using a gene signature of 435 probesets (398 genes) that was used to distinguish the 4 gene expression-based clusters of T-ALL in Homminga et al. Cancer Cell 2011. (d) Hierarchical clustering analysis (PearsonWards method) using 107 genes that overlap between the 547-gene signature and the T-ALL signature of 435 genes probesets. The dendogram at the left represents the similarity of expression of 107 genes across the 117 T-ALL patient samples. The dendogram at the top indicates how related are the 117 T-ALL patients samples and the respective cluster is indicated. (e) Schematic representation of the different subtypes of T-ALL with the corresponding developmental arrest stages provoked by the ectopic expression of the oncogenes. The four T-ALL clusters are also separated into pre-commitment and post- commitment T-cell developmental programs.

Figure 2. Constitutive Expression of MEF2C, LYL1, or LMO2 impairs T-cell development (a) Schematic representation of the elements cloned into our Gateway compatible LeGO-DEST vector. The 3 lentiviral expression constructs coding the oncogenes LMO2, LYL1 or MEF2C exhibit 4830 bp, 5190 bp and 5750 bp of proviral RNA lengths, respectively. The BFP control vector composed by the SFFV promoter followed by the BFP reporter contains 4215 bp. SFFV: Spleen Focus Forming Virus promoter, T2A: Thosea asigna virus 2A element, BFP: blue fluorescent protein. Grey boxes indicate lentiviral elements in LeGO-DEST. (b) Top panel: flow- cytometry image demonstrating BFP- and BFP+ (transduced) Jurkat cells; bottom panel: Western-blot of lysates from Jurkat cells transduced with MEF2C, LYL1, or LMO2 lentiviral 128

constructs and the SFFV-BFP control. After cleavage of the proteins, the MEF2C, LYL1, or LMO2 retain the majority of the T2A peptide that can serve as an epitope tag and can be recognized by the anti-2A antibody (left blot). The BFP reporter that folllows the T2A sequence is separated and not recognized by anti-2A. Direct antibodies against MEF2C, LYL1, or LMO2 were used to detect the expression of those proteins in transduced Jurkat cells.

Figure 3. Constitutive Expression of MEF2C, LYL1, or LMO2 impairs T-cell development (a) Schematic representation of the OP9-DL1 co-cultures used to differentiate transduced HSCs into consecutive T-cell differentiation stages. Briefly, HSC were transduced with the different lentivirus constructs and cultured on top of OP9-DL1 cells for 18 days until the cells reached the CD1a+ stage. (b) Schematic representation of the flow-cytometry analysis used to distinguish the different populations present in the co-cultures. The use of CD34 and CD7 antibodies allows the detection of 4 different populations: HSCs (CD34+CD7-), non-T-cell populations (CD34-CD7-), ETPs (CD34+CD7+) and T-cells (CD34-CD7+). (c) Transduced cells are illustrated as BFP- positive cells in flow-cytometry analysis. Analysis of transduced cells is depicted as blue dots on the right graphs and analysis of non-transduced cells is depicted as pink dots on the left graphs. The two top-graphs correspond to the number of T-cells (CD7+CD34+/-) and the two bottom graphs correspond to the number of non T-lineage cells (CD7-CD34-) in 3 independent single well experiments for each construct over 18 days of co-culture (n=3). (d) Flow-cytometry plots illustrating distinct T-cell differentiation stages based on the expression of CD34 and CD7 markers at days 5, 9 and 12. Cells transduced with the control vector are on top and cells transduced with lentiviral constructs expressing oncogenes are at the bottom. All populations were also gated for CD45+GFP- to exclude OP9-DL1 cells that are GFP positive.

Figure 4. Constitutive expression of MEF2C, LYL1, or LMO2 is dependent on ETP- transcriptional programs to promote differentiation block before T-cell commitment (a) Immunophenotype of 7 T-cell subsets (A-F) measured over time in OP9-DL1 co-cultures based on surface markers CD45, CD34, CD7, CD5, CD1a, and CD44. (b) Total cell numbers of distinct T-cell subsets (A-F, all BFP+ gated) upon transduction of HSCs with control-, MEF2C-, LYL1-, or LMO2-lentiviral vectors and differentiation in OP9-DL1 during 18 days. All subsets were also gated on CD45+ and GFP- to exclude OP9-DL1 cells. (c) Schematic representation of the differentiation block caused by constitutive expression of MEF2C, LYL1, or LMO2 before T-cell commitment.

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Chapter 7

General discussion & Future perspectives

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General discussion and future perspectives

Over the last decades, diverse genomic aberrations were identified and characterized in T-ALL patient samples, resulting in the activation of specific oncogenes or the inactivation of tumor suppressor genes. The overexpression of rearranged oncogenic transcription factors classify distinct genetic T-ALL subgroups that harbor specific gene expression profiles. These aberrations (type-A) are considered the driving oncogenic events, which are associated with arrest at specific stages of T-cell differentiation. In addition, recurrent genetic aberrations that affect cell viability and/or proliferation are denoted as type B hits, and are found in nearly all T- ALL genetic subgroups. Despite the extensive identification of genetic defects driving human T- ALL, multi-agent chemotherapy sometimes combined with stem cell transplantation is the standard treatment for pediatric T-ALL patients. Besides the risk for severe acute toxicities and long-term side effects that are associated with this intensive treatment, still 15% of the patients relapse and die due to therapy resistance. The current challenge is therefore to search for alternative patient-tailored treatment regimens that may reduce toxicity or enhance the cellular response to chemotherapeutic agents. To achieve this we need to understand how those genetic aberrations occur, which oncogenic mechanisms are being activated, and how these events contribute to leukemogenesis, disease progression or relapse. This thesis clearly shows the benefits of using in vitro methodologies to understand the impact of oncogenic mechanisms and their contribution to leukemogenesis. These data may therefore contribute to the development of novel targeted therapies.

The impact of type B mutations in T-ALL heterogeneity and clonal evolution

Clonal genetic diversity is a common feature of cancer and is probably, from a Darwinian natural selection perspective, the essential substrate for clonal evolution, disease progression and relapse. T-ALL is a genetically complex type of leukemia, exhibiting a mixture of leukemic subclones carrying different combinations of genetic lesions at diagnosis that are under constant selection pressure during chemotherapeutic treatment. In contrast to type-A mutations, which are considered the driving genetic events present in all cells of the leukemia, type-B mutations may not be clonal and are frequently acquired or lost during disease progression as result of clonal evolution. Studies on clonal evolution are possible nowadays due to advances in Next- generation sequencing (NGS) techniques and the development of bioinformatic tools that can distinguish these mutations at the clonal and subclonal levels. Identifying the full complexity of 135

subclonal architecture and genetic diversity in T-ALL can be done through deep sequencing of entire tumor cell populations. This approach allows to discriminate subclonal passenger mutations from the oncogenic driver mutations that most likely contribute to disease progression and therapy resistance.1 Targeted therapy directed against mutant molecules may have limited efficacy if the targets themselves are not initiating lesions, but reflect secondary mutations occuring in leukemia subclones. Because genetic variation may represent a significant obstacle to effective therapy, WGS will certainly support personalized targeted therapy, namely the combination of specific signaling inhibitors. For instance, NOTCH1-activated leukemias should be screened for the presence of subclonal PTEN aberrations – such as PTEN microdeletions described in Chapter 3 – since newly acquired PTEN mutations may drive resistance to NOTCH1-targeting therapies as discussed in Chapter 2. In this case, combined NOTCH and AKT-directed therapies may prove an effective treatment relapse of T-ALL blasts (Chapter 2). Different lines of research are currently focusing on the understanding of molecular mechanisms that are associated with chemotherapy resistance, as the biological basis for disease relapse in T-ALL is still poorly understood. Recently, whole-exome sequencing of matched diagnosis, remission and relapse DNA samples, identified recurrent mutations that are acquired in relapse samples after chemoterapy, indicating that relapse is not only mediated by minor leukemic subclones that are already present at diagnosis. Specifically, activating mutations in the cytosolic 5′-nucleotidase II gene (NT5C2) were found in about 20% of relapsed T-ALL patients and are associated with the outgrowth of 6-mercaptopurine (6-MP)- or 6-thioguanine (6-TG)- resistant clones.2,3 NT5C2 encodes a 5′-nucleotidase enzyme that is responsible for the inactivation of nucleoside-analog chemotherapy drugs routinely used in T-ALL maintenance therapy, including 6-MP and 6-TG.4,5 Accordingly, expression of NT5C2 mutant in ALL lymphoblasts resulted in increased nucleotidase activity in vitro confering resistance to 6-MP and 6-TG.2,3 These results demonstrated the impact of increased nucleoside-analog metabolism in disease progression and chemotherapy resistance. Furthermore, increasing evidence is demonstrating the role of leukemic stem cells (LSC),6-8 epigenetic changes9,10 and metabolism alterations9 in the evolution of resistant leukemic clones. Overall, the identification and comprehension of molecular mechanisms involved in disease progression and therapy- resistance is critical for the development of new strategies that may prevent the acquisition or outgrowth of treatment resistant subclones.

Genetic abnormalities in normal healthy tissues

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In our studies in Chapter 3 we reveal that PTEN microdeletions can also occur in the thymus of normal healthy thymocytes. This observation together with other studies11,12 show that healthy human thymocytes are also prone to illegitimate RAG-mediated recombination events as observed in T-ALL blasts, resulting in the inactivation of tumor suppressor genes or activation of oncogenes. However, in healthy thymocytes, those mutations may occur as single events or in cells that lack essential collaborating mutations to become a pre-leukemic cell. As proposed by Mel Greaves13 this phenomena corroborates the vulnerability of human tissues to oncogenic processes, which is supported by the high prevalence of “hidden” malignant cancers or premalignant carcinomas identified in organs of individuals dying of non-cancer causes. In line with this, the author defends that the process of tumorigenesis is a lottery, since most tumors never emerge all the bottlenecks. As an example, the screening of mutations in archived neonatal blood spots or Guthrie cards showed that many healthy babies carry pre-leukemic translocations such as TEL/AML gene fusion, but never develop leukemias as the secondary hits never occur.14,15 Hence, cancer is commonly or ubiquitously initiated but promotion to full malignancy is rare as the emergence of a cancer clone is a relatively inneficient evolutionary process. Accordingly, despite the frequent acquisition of PTEN microdeletions in thymocytes, this event by itself is not sufficient to enable increased cell survival or proliferation that leads to development of T-ALL. To become malignant, developing thymocytes with PTEN inactivation may require the collaboration with specific oncogenes (type A aberrations), such as TAL or LMO genes, that promote a late developmental arrest and allow the acquisition of additional genetic hits. Late T-cell development stages (post-commitment program) are especially prone to such oncogenic chromosomal translocations, because the “recombination machinery” that is responsible for TCR recombinations (i.e. V(D)J rearrangements) and for these leukemogenic events is especially expressed after T-cell commitment.

Normal T-cell development as a reference to elucidate the role of T-ALL oncogenes in leukemogenesis

In this thesis we have used two different approaches to understand the role of oncogenic transcription factors (Type-A aberrations) in T-ALL. First, we determined the expression of those oncogenes in normal T-cell development (Chapter 5); second, we examined the associations of these oncogenes with particular T-cell developmental programs that may be required for leukemogenesis and accordingly, we tested the enforced expression of ETP-ALL oncogenes during early T-cell development (Chapter 4 and 6). In the first approach (Chapter 5), supervised

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cluster analysis of consecutive human T-cell differentiation stages in the OP9-DL1 system demonstrated that in vitro development closely resembles the in vivo situation both in human and mice. It therefore validates the OP9-DL1 culture system using umbilical cord blood-derived HSCs to study human T-cell development. Gene expression analysis exhibits the pattern of expression of the proto-oncogenes and other transcription factors throughout normal T-cell development, and helps to comprehend the function of expressed oncogenes in the different stages of the developmental process. Although some oncogenes as TLX1, TLX3, NKX2.1 and fusion proteins are not expressed during normal T-cell development, their ectopic expression in malignant thymocytes may interfere with important T-cell development regulators that are expressed during normal T-cell development. For instance, it has been described that TLX1 and TLX3 can interact with ETS1, which is a critical component of the complex that binds and activates the TCR enhancer element (Eα), resulting in an enrichment of repressive histone modifications and lack of TCRα gene expression that ultimately leads to a differentiation arrest.16 Accordingly, in order to distinguish the activation of particular oncogenic mechanisms upon ectopic expression of the oncogenes and to understand how their abnormal regulation can promote leukemogenesis, it is crucial to fully understand normal T-cell development. Remarkably, we identified the T-cell lineage commitment point in humans to occur during the CD7+CD5+stage, precisely when cells downregulate the expression of CD44 at the surface membrane. This exactly resembles the DN2a-DN2b transition that has been revealed as T-cell commitment point in mice. Despite a previous study that described a differential expression of CD44 earlier than CD1a expression in human T-cell development,17 here we place CD44 expression in the context of other T-cell development markers, which allows to distinguish consecutive T-cell developmental stages. More importantly, we functionally demonstrated that loss of CD44 marks human T-cell lineage commitment. This contributed to define pre- and post- commitment programs of T-cell development and helps understanding the association of different oncogenes with these particular programs. Defining the consecutive stages of normal T-cell development is also particularly useful when testing the leukemogenic effects of oncogenic transcription factors in vitro, as shown by the developmental arrest at the ETP-stage upon ectopic expression of MEF2C, LYL1 or LMO2. Overall, these results can be potentially interesting for the field of normal human T-cell development and accompanying T-cell (developmental) malignancies.

The oncogenic effect of T-ALL oncogenes depends on specific developmental programs

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Furthermore, gene expression analysis of the consecutive T-cell stages obtained with the OP9- DL1 in vitro system allowed us to identify a gene expression signature of 1387 genes that cluster human T-ALL samples in four T-cell developmental clusters. Importantly, these clusters are nearly identical to the 4 genetic subgroups (ETP-ALL, TLX, proliferative and TALLMO) that we previously identified using unsupervised hierarchical clustering analysis of 117 pediatric T-ALL samples.18 Particularly, samples that are classified as ETP-ALL or TLX are characterized by the expression of a pre-commitment profile, while proliferative and TALLMO patient samples exhibited a post-commitment profile. This was expected for ETP-ALL, since it is the most immature T-ALL entity, but more importantly it supports our hypothesis that γδ-lineage cells – characteristic of the TLX phenotype – diverge from αβ-development before T-cell commitment. This hypothesis is supported by genetic analysis of T-ALL patient samples: TLX and ETP-ALL patients are frequently characterized by BCL11B enhancer-driven translocations to TLX3 or other genes (NKX2.5, SPI1, LMO2). In contrast, TALLMO and proliferative subtypes frequently have TCR-driven rearrangements, supporting a pathogenic role of RAG1/2 proteins in pre- leukemic T-cells that have passed T-cell commitment. Remarkably, about half of the genes represented in the gene signatures obtained from in vitro T-cell development data overlapped with the T-ALL gene signature composed of 435 probesets that previously led to the identification of the four T-ALL genetic subgroups.18 T-ALL oncogenic aberrations are therefore closely associated with pre- or post-commitment developmental programs. This supports our hypothesis that oncogenes need to cooperate with those programs for leukemogenesis, and actually may only exert their leukemogenic role in the context of specific T-cell developmental programs. This may help to understand how the oncogenes can block T-cell differentiation, and to what extent they are responsible for driving the expression of genes in these programs. Regarding this, we have physiologically tested the leukemogenic potential of MEF2C, LYL1 or LMO2 using the OP9-DL1 system, providing evidence that all three are able to block T-cell development within the ETP-stage. Contrarily, ectopic expression of these genes in thymocytes that already passed the ETP-stage revealed that these oncogenes did no longer affect the developmental process as cells could normally differentiate into the T-cell lineage. Accordingly, we conclude that solely the ectopic expression of the oncogene is not sufficient for leukemogenesis, but instead it requires a specific transcriptional program characteristic of distinctive T-cell developmental stages to cause a differentiation arrest. Moreover, although MEF2C overexpression was capable of arresting the cells at a very immature stage, those cells eventually disappeared from culture. Probably, those cells require extra signals for survival and proliferation that are usually derived from type B

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mutations in T-ALL, such as the IL7R or RAS mutations found in ETP-ALL. Importantly, if we understand the oncogenic mechanisms of type A mutations, we could alleviate the developmental block of leukemic cells by targeting critical downstream molecules that are responsible for differentiation arrest. This may not only impair the acquisition of additional genetic hits, but also prevent the uncontrolled proliferation of cells at that stage. Moreover, it would be interesting to further investigate the genes that are contained in the T-ALL gene signature (435 probesets), especially after filtering out all genes that are associated with T-cell development. Studying the resultant genes could provide new insights into important oncogenic mechanisms that are specific for each T-ALL subgroup, such as the activation of critical proliferation or survival signaling molecules, e.g. MYC and TP53. This would indicate that there are alternative pathways apart from pre- or post-commitment networks that are prone to become mutated in each T-ALL subtype to promote proliferation and/or survival mechanisms. Overall, this could give a better rationale for the use of specific inhibitors to different T-ALL entities.

Current methodologies to unravel the leukemogenic mechanisms of ETP-ALL

Despite the important insights obtained on the leukemogenic potential of ETP-ALL oncogenes using the OP9-DL1 in vitro system, some questions remain to be investigated. First, we would like to examine the combinations of oncogene and type B aberrations that are essential do drive leukemogenesis, and investigate the downstream activation of oncogenic signaling mechanisms. Second, it is important to develop models to test the efficacy of specific pathway inhibitors in T-ALL. We are currently using in vitro techniques in T-ALL cell lines to characterize the transcriptional complex that comprises MEF2C, to investigate molecular interactions between MEF2C and other proteins that participate in an oncogenic transcription complex (e.g. LYL1 and LMO2). This may help to identify new therapeutic targets that could dismantle such a complex. Furthermore, it may lead to a better understanding on how MEF2C drives particular genes at the ETP-stage, which can be additional targets for therapy. In addition, we are working with in vivo mouse models that contain conditional deletion (KO) and overexpression (KI) of MEF2C to understand the role of this oncogene in normal and malignant in vivo T-cell development, respectively. Gene expression analysis of leukemic or pre-leukemic (potentially arrested) thymocytes (KI) will determine the effects of enforced expression of MEF2C transcriptional complex components or critical downstream targets on T-cell proliferation and differentiation.

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Thus, MEF2C-driven leukemias in these mice will form a relevant model to investigate and characterize oncogenic MEF2C transcriptional complexes and its downstream target genes. It can also represent an important in vivo model to test the effects of compounds against relevant MEF2C-driven oncogenic pathways. In addition, we can further explore the OP9-DL1 in vitro to test the expression of ETP- ALL oncogenes in combination with frequent ETP-ALL type-B aberrations, such as IL7R or RAS activating mutations. Although, we have shown that MEF2C-, LYL1- or LMO2- overexpression at the very early stages of T-cell development was capable of promoting a differentiation arrest, it would be interesting to transduce arrested cells with other collaborative events such as IL7R or RAS activating mutations to promote cell proliferation and survival. This system could provide new insights into the downstream molecular pathways that are activated to promote leukemogenesis and it may be a useful tool for testing drugs that target (pre-)leukemic cells. Alternatively, cells expressing the combination of oncogenes and type-B mutations could be xenotransplanted into sublethally irradiated mice to further examine the oncogenic potential of different mutations and the resulting malignancies at a cellular and molecular level.

Future research in T-ALL

Despite a detailed understanding of the genetic defects that drive T-ALL, we face a number of interesting challenges. First, we need to better understand how specific T-ALL oncogenes and tumor suppressors cooperate to drive full leukemic transformation. We still lack detailed information whether the co-occurrence of certain mutations may be synergistic in the activation of molecular oncogenic mechanisms that transform normal developing T-cells into (pre)- leukemic blasts. Secondly, the potential contribution of the exact order in which genetic lesions are acquired during disease initiation, progression, and/or maintenance, which underlie disease subclonality and evolutionaly selection before and after therapy, should also be further investigated. Moreover, the existence of leukemic initiating cells (LICs) in the context of T-ALL is still under debate, and the molecular mechanism that could regulate LIC activity remains poorly characterized. Furthermore, deregulated expression of microRNAs, long non-coding RNAs, enhancer activities, aberrant chromatin remodeling, and/or epigenetic changes in the context of malignant T-cell transformation need to be further investigated.

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References

1. Ding L, Ley TJ, Larson DE, et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 2012;481(7382):506-510. 2. Tzoneva G, Perez-Garcia A, Carpenter Z, et al. Activating mutations in the NT5C2 nucleotidase gene drive chemotherapy resistance in relapsed ALL. Nat Med. 2013;19(3):368- 371. 3. Meyer JA, Wang J, Hogan LE, et al. Relapse-specific mutations in NT5C2 in childhood acute lymphoblastic leukemia. Nat Genet. 2013;45(3):290-294. 4. Pieters R, Huismans DR, Loonen AH, et al. Relation of 5'-nucleotidase and phosphatase activities with immunophenotype, drug resistance and clinical prognosis in childhood leukemia. Leuk Res. 1992;16(9):873-880. 5. Pieters R, den Boer ML, Durian M, et al. Relation between age, immunophenotype and in vitro drug resistance in 395 children with acute lymphoblastic leukemia--implications for treatment of infants. Leukemia. 1998;12(9):1344-1348. 6. King B, Trimarchi T, Reavie L, et al. The ubiquitin ligase FBXW7 modulates leukemia- initiating cell activity by regulating MYC stability. Cell. 2013;153(7):1552-1566. 7. Roderick JE, Tesell J, Shultz LD, et al. c-Myc inhibition prevents leukemia initiation in mice and impairs the growth of relapsed and induction failure pediatric T-ALL cells. Blood. 2014;123(7):1040-1050. 8. Schubbert S, Cardenas A, Chen H, et al. Targeting the MYC and PI3K Pathways Eliminates Leukemia-Initiating Cells in T-cell Acute Lymphoblastic Leukemia. Cancer Res. 2014;74(23):7048-7059. 9. Peirs S, Van der Meulen J, Van de Walle I, et al. Epigenetics in T-cell acute lymphoblastic leukemia. Immunol Rev. 2015;263(1):50-67. 10. Knoechel B, Roderick JE, Williamson KE, et al. An epigenetic mechanism of resistance to targeted therapy in T cell acute lymphoblastic leukemia. Nat Genet. 2014;46(4):364-370. 11. Marculescu R, Le T, Simon P, Jaeger U, Nadel B. V(D)J-mediated translocations in lymphoid neoplasms: a functional assessment of genomic instability by cryptic sites. J Exp Med. 2002;195(1):85-98. 12. Marculescu R, Vanura K, Le T, Simon P, Jager U, Nadel B. Distinct t(7;9)(q34;q32) breakpoints in healthy individuals and individuals with T-ALL. Nat Genet. 2003;33(3):342-344. 13. Greaves M. Darwinian medicine: a case for cancer. Nat Rev Cancer. 2007;7(3):213-221. 14. Gale KB, Ford AM, Repp R, et al. Backtracking leukemia to birth: identification of clonotypic gene fusion sequences in neonatal blood spots. Proc Natl Acad Sci U S A. 1997;94(25):13950-13954. 15. Wiemels JL, Cazzaniga G, Daniotti M, et al. Prenatal origin of acute lymphoblastic leukaemia in children. Lancet. 1999;354(9189):1499-1503. 16. Dadi S, Le Noir S, Payet-Bornet D, et al. TLX homeodomain oncogenes mediate T cell maturation arrest in T-ALL via interaction with ETS1 and suppression of TCRalpha gene expression. Cancer Cell. 2012;21(4):563-576. 17. Marquez C, Trigueros C, Fernandez E, Toribio ML. The development of T and non-T cell lineages from CD34+ human thymic precursors can be traced by the differential expression of CD44. J Exp Med. 1995;181(2):475-483. 18. Homminga I, Pieters R, Langerak AW, et al. Integrated transcript and genome analyses reveal NKX2-1 and MEF2C as potential oncogenes in T cell acute lymphoblastic leukemia. Cancer Cell. 2011;19(4):484-497.

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Chapter 8

Summary / Samenvatting Supplementary Data Curriculum Vitae List of publications Portfolio Acknowledgements

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Summary

This thesis aimed to: 1) identify additional PTEN-inactivating events as novel type-B mutations that may provide survival and proliferation advantage to (pre)-leukemic cells, and understand their clinical impact in T-ALL; 2) to establish an in vitro co-culture system (OP9-DL1) that supports T-cell development from human umbilical cord blood-derived hematopoietic stem cells (HSCs), with the intention of defining distinct human T-cell development stages in relation to the underlying transcriptional programs and expression of cell surface markers; 3) to optimize a lentivirus system that supports transduction and stable expression of oncogenes in human and mouse HSCs and 4) to study the impact of ETP-ALL expressed oncogenes (such as MEF2C, LYL1 or LMO2) on human T-cell development using the OP9-DL1 in vitro system.

Chapter 2. The tumor suppressor PTEN negatively regulates PI3K-AKT signaling and is often inactivated by mutations (including deletions) in a variety of cancer types, including T-ALL. In chapter 2, we review mutation-associated mechanisms that inactivate PTEN together with other molecular mechanisms that activate AKT and contribute to T-cell leukemogenesis. In addition, we discuss how Pten mutations in mouse models affect the efficacy of gamma-secretase inhibitors (GSI) to block NOTCH1 signaling through activation of AKT. Based on these models and on observations in primary diagnostic T-ALL patient samples, we propose that PTEN- deficient cells employ a compensatory mechanism in which PI3K-AKT signaling is dampened over time. As a result of this reduced PI3K-AKT signaling, the level of AKT activation is insufficient to compensate for NOTCH1 inhibition, resulting in responsiveness to GSI. On the other hand, de novo acquired PTEN inactivating events in a NOTCH1-dependent T-ALL result in immediate, strong activation of PI3K-AKT signaling, increased glycolysis and glutaminolysis, and consequently GSI resistance. Due to the central role of PTEN-AKT signaling in T-ALL and in resistance to NOTCH1 inhibition, AKT inhibitors may be a promising addition to current treatment protocols for T-ALL.

Chapter 3. Phosphatase and tensin homolog (PTEN)-inactivating mutations and/or deletions are an independent risk factor for relapse of T-cell acute lymphoblastic leukemia (T-ALL) patients treated on Dutch Childhood Oncology Group or German Cooperative Study Group for Childhood Acute Lymphoblastic Leukemia protocols. Some monoallelic mutated or PTEN wild-type patients lack PTEN protein, implying that additional PTEN inactivation mechanisms exist. We show that

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PTEN is inactivated by small deletions affecting only a few exons in 8% of pediatric T-ALL patients. These so-called microdeletions were clonal in 3% and subclonal in 5% of patients. Conserved deletion breakpoints were identified that are flanked by cryptic recombination signal sequences (cRSSs) and frequently have non-template-derived nucleotides inserted in between breakpoints, pointing to an illegitimate RAG recombination activity. Identified cRSSs drive RAG- dependent recombination in a reporter system as efficient as bona fide RSSs elements that flank T-cell receptor locus gene segments. Microdeletions strongly associate with the TALLMO subtype that is characterized by TAL1 or LMO2 rearrangements, in line with the previous association of PTEN non-sense mutations or entire locus deletions. Primary and secondary xenotransplantation of TAL1- rearranged T-ALL allowed development of leukemic subclones that acquired new PTEN microdeletions, indicating that PTEN aberrations may be continuously acquired and a mechanism for subclonal diversity. Remarkably, equivalent microdeletions were also detected in thymocytes of healthy individuals indicating that ongoing RAG activity may actively contribute to the acquisition of preleukemic hits in normal thymocytes that will be effectively eradicated by the immune system.

Chapter 4. Leukemia research increasingly depends on techniques to induce or to inhibit expression of oncogenes or tumor-suppressors in HSCs or primary leukemic cells. Surprisingly little information is available to optimize lentiviral transduction of primary leukemic cells or HSCs. We have therefore carefully optimized transduction of murine and human HSCs using an optimized lentiviral vector design, serum-free virus production of recombinant viruses and quantitation, and optimized delivery (transduction) into primary human or mouse HSCs. We developed a quantitative RT-PCR-method to quantify the encapsulated proviral RNA particles. We have observed an inverse exponential relation between the length of the proviral RNA encoded by the construct and the number of viral particles that are being produced in HEK293T- cells.The length of the proviral RNA also determines the transduction efficiency of target cells. Increased concentrations of serum (ranging from 0% to 10%) during transduction negatively affect the transduction rate of human HSCs. In conclusion, we provide an optimized serum-free lentiviral gene transfer into human and murine hematopoietic stem cells, where the reduction of lentiviral constructs size will facilitate efficient transfer of large oncogene sequences into human or mouse HSCs or primary leukemic cells.

Chapter 5. Human T cell development is less well studied than its murine counterpart due to the lack of genetic tools and the difficulty of obtaining cells and tissues. However, recent

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technological advances allow identification of the transcriptional landscape of differentiating human thymocytes. Here we report the gene expression profiles of 11 immature, consecutive T- cell developmental stages that were obtained using the OP9-DL1 in vitro culture system. The changes in gene expression of cultured stem cells on OP9-DL1 match those of ex vivo isolated human thymi. These analyses let us to define a gene signature that distinguishes pre- and post- αβ T-cell commitment. We found that loss of CD44 marks T-cell commitment in early CD7+CD5+ cells, before the acquisition of CD1a surface expression. The CD44-CD1a- post- committed thymocytes have initiated in frame TCR rearrangements and have completely lost the capacity to develop into myeloid, B- and NK-cells, unlike uncommitted CD44+CD1a- thymocytes. Therefore, loss of CD44 represents a previously unrecognized stage that defines the earliest committed T-cell population in the human thymus.

Chapter 6. Pediatric T-ALL is a cancer of developing T-cells in the thymus. The T-ALL subtypes are characterized by specific chromosomal rearrangements driving the ectopic expression of oncogenes that result in arrest of leukemic cells at specific T-cell developmental stages. Here, we show that T-ALL subtypes are strongly associated with the expression of particular T-cell developmental programs. Immature/ETP-ALL and TLX subtypes of T-ALL express a pre- commitment program whereas the proliferative and TALLMO subtypes express a post- commitment program. This suggests that T-ALL oncogenes require the expression of particular T-cell transcriptional programs to be able to exert an oncogenic function, and therefore may hijack T-cell developmental programs by facilitating a developmental arrest. That is further supported by functional in vitro studies where we investigate the impact of typical ETP-ALL (proto-)oncogenes, including MEF2C, LYL1 or LMO2 on early T-cell development. Umbilical cord blood stem cells transduced with lentiviral expression constructs that constitutively drive MEF2C, LYL1 or LMO2 were cultured on OP9-DL1 cells for differentiation towards the T-cell lineage. Expression of MEF2C or its putative co-factors (LYL1 or LMO2) blocks T-cell development at ETP-stages, in line with their roles in immature T-ALL. Contrarily, expression of the oncogenes in T-cells beyond the ETP-stages does no longer support T-cell development arrest, indicating that their leukemogenic potential is dependent on ETP, pre-commitment transcriptional networks. Thus, we conclude that the specific interaction of an aberrant oncogenic transcription factor with specific transcriptional network in a developing T-cell is an important requirement for oncogenesis.

Chapter 7. The findings described in the thesis are discussed and put into perspective.

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Nederlandse samenvatting

Dit proefschrift heeft de volgende doelstellingen gehad: 1) Het identificeren van bepaalde afwijkingen in het PTEN gen, in toevoeging van de al bekende genetische afwijkingen, die als nieuwe type-B mutaties een overlevings- of groeivoordeel zouden kunnen bieden voor (pre-) leukemische cellen. Ook het begrijpen en beschrijven van de klinische impact van PTEN afwijkingen in T-cel acute leukemie (T-ALL) patiënten behoorde tot deze doelstelling. 2) Het opzetten van een kweeksysteem (OP9-DL1) waarin bloedstamcellen verkregen uit navelstrengbloed in een petrischaal gedurende enkele weken kunnen uitgroeien tot T-cellen. Het bepalen van genexpressieprofielen van de verschillende T-cel ontwikkelingsstadia aan de hand van specifieke oppervlakte markers heeft het functioneren van het systeem gevalideerd en ook geleid tot de identificatie van een extra oppervlakte marker. 3) De optimalisatie van een methode waarmee met behulp van lenti-virussen bepaalde oncogenen ingebracht en geactiveerd kunnen worden in bloedstamcellen van menselijke en muizen oorsprong. 4) Het bestuderen van het effect van de activatie van oncogenen die specifiek voorkomen in een vroege vorm van T-ALL (genaamd ETP-ALL) op de menselijke T-cel ontwikkeling in het OP9- DL1 kweeksysteem.

Hoofdstuk 2. De functie van het zogenaamde ‘tumor suppressor gen’ PTEN is het remmen van een belangrijk signalerings-pad PI3K-AKT dat na activatie de overleving en groei van cellen stimuleert. In T-ALL, net als in verscheidene andere kanker soorten, komen afwijkingen voor die leiden tot het inactief of afwezig zijn van PTEN, en daardoor tot verminderde remming van het PI3K-AKT pad. In hoofdstuk 2 hebben we de literatuur samengevat rondom PTEN, PI3K-AKT en een ander belangrijk signalerings-pad dat begint bij de NOTCH1 receptor. We behandelen hoe bepaalde mutaties en andere afwijkingen in deze verschillende paden samen kunnen leiden tot de ontwikkeling van T-ALL, en hoe verschillende remmers aangrijpen op deze mechanismes en dus mogelijk klinisch relevant zijn. We concluderen dat AKT remmers veelbelovend kunnen zijn als toevoeging aan de huidige behandelingsprotocollen voor T-ALL.

Hoofdstuk 3. T-ALL patiënten met PTEN afwijkingen die in Nederland en Duitsland zijn behandeld hebben een verhoogd risico op terugval. Hoewel veel van deze afwijkingen verklaard kunnen worden door genetische mutaties of deleties van het PTEN gen die resulteren in het

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niet-functioneren of afwezig zijn van het PTEN eiwit, zijn er ook cellen van patiënten zonder een dergelijke verklaring voor de afwezigheid van PTEN. We hebben in deze cellen hele kleine, gedeeltelijke deleties in het PTEN gen gevonden die ook leiden tot de afwezigheid van PTEN. Deze zogenaamde ‘micro-deleties’ werden met de eerdere technieken over het hoofd gezien, en komen voor in 8% van kinderen met T-ALL. We hebben laten zien dat deze micro-deleties worden veroorzaakt door een herschikkings-proces wat normaliter de herschikking van T- en B- cel receptor genen verzorgt en dus een normaal actief proces is in rijpende T-cellen. Opmerkelijk genoeg hebben we deze PTEN-microdeleties ook gevonden in sommige rijpende T- cellen van enkele gezonde kinderen, wat aangeeft dat er meerdere mutaties moeten optreden voordat deze microdeleties een risico kunnen worden.

Hoofdstuk 4. Ten behoeve van ons T-ALL onderzoek naar het effect van de activatie van bepaalde oncogenen introduceren (‘transductie’) we grote DNA moleculen in normale bloedstamcellen met behulp van specifieke lenti-virussen. Omdat deze techniek uiterst inefficiënt is in stamcellen, hebben we het door meerdere aanpassingen verbeterd. We hebben de volgende facetten geoptimaliseerd: ontwerp lenti-virale DNA vector, virus productie, nauwkeurige bepaling hoeveelheid virus, transductie in bloedstamcellen. We hebben gezien dat met toenemende lengte van de vector zowel de virus productie alsook de transductie efficiëntie afneemt. Om toch grote oncogenen te kunnen bestuderen in bloedstamcellen, zullen andere elementen uit de DNA vector verkleind of verwijderd moeten worden om een betere transductie efficiëntie te realiseren.

Hoofdstuk 5. T-cel ontwikkeling/rijping in mensen vindt plaats in de thymus, voornamelijk gedurende de eerste 10 levensjaren. Vergeleken met het onderzoek naar T-cel ontwikkeling in de muis is de menselijke T-cel ontwikkeling weinig bestudeerd, gedeeltelijk omdat primair materiaal van kinderen weinig beschikbaar is. Met de opkomst van geavanceerde kweeksystemen (OP9-DL1) kan ook de menselijke T-cel ontwikkeling nagebootst en bestudeerd worden in een petrischaal. We hebben dit systeem opgezet en gebruikt om genexpressieprofielen van meerdere opeenvolgende menselijke T-cel ontwikkelingsstadia (aan de hand van oppervlakte markers) te bepalen. Uit de analyse van deze profielen blijkt dat ook op het niveau van de genexpressie het kweeksysteem de werkelijke ontwikkeling goed weergeeft. Bovendien heeft de analyse geleid tot een extra oppervlakte marker CD44 die onze genexpressieprofielen van voor en na een belangrijk checkpunt in T-cel ontwikkeling, de zogenaamde ‘T-cel commitment’, scheidt. We hebben getest dat de vroegste cellen in de

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thymus die nog CD44 hebben, ook nog kunnen uitrijpen naar andere bloedceltypes; de iets latere ‘gecommitteerde’ T-cellen hebben geen CD44 en kunnen alleen nog T-cellen worden.

Hoofdstuk 6. T-ALL in kinderen kan, gebaseerd op genexpressieprofielen, grofweg in vier subgroepen worden verdeeld: de meest vroege ETP-ALL, TLX groep, proliferatieve groep, en de TALLMO groep. De TLX en TALLMO groep zijn genoemd naar de belangrijkste oncogenen die de T-ALL veroorzaken: TLX3, TAL1 en LMO2. De proliferatieve groep wordt gekenmerkt door de oncogenen TLX1 en NKX2.1 terwijl de meest vroege ETP-ALL groep gekenmerkt wordt door MEF2C, LYL1 en ook LMO2. We hebben de genexpressieprofielen van voor en na normale T- cel commitment (hoofdstuk 5) gebruikt om de T-ALL patiënten opnieuw te klusteren en vonden grofweg dezelfde vier groepen terug. De ‘vroege’ ETP-ALL en TLX groepen overlappen met het profiel ‘voor-commitment’, terwijl de ‘latere’ proliferatieve en TALLMO groepen overlappen met het ‘na-commitment’ profiel. Dit suggereert dat de T-ALL oncogenen functioneren in de context van normale T-cel ontwikkelingsgenen die ook actief zijn, en dat T-ALL oncogenen het normale ontwikkelingsprogramma blokkeren in specifieke stadia. Gebruikmakend van de expertise ontwikkeld in hoofdstukken 4 en 6, hebben we dit nagebootst voor ETP-ALL, waarin de oncogenen MEF2C, LYL1 en LMO2 actief zijn. We hebben deze oncogenen ingebracht in normale bloedstamcellen middels virale transductie, en vervolgens de ontwikkeling gevolgd in het OP9-DL1 kweeksysteem. Deze oncogenen leiden alle drie tot een blokkade vroeg in de ontwikkeling, terwijl ze geen effect hebben als ze pas in latere (na ‘commitment’) cellen aangezet worden. Deze observatie ondersteunt de hypothese dat de normale T-cel ontwikkeling alleen geblokkeerd wordt door specifieke oncogenen in dát specifieke stadium waarin het normale ontwikkelingsprogramma actief is en het de functie van het oncogen en de leukemie ontwikkeling ondersteunt.

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Supplementary Data

Chapter 3 Supplementary Materials and Methods

Multiplex ligation-dependent probe amplification (MLPA) reaction For MLPA analysis, the SALSA MLPA kit (MRC-Holland, Amsterdam, the Netherlands) was used in combination with the P200-A1 Human DNA reference-1 probemix kit (MRC-Holland). Two synthetic left and right hybridization probe oligonucleotide (LPO and RPO) pairs were designed per exon for all 9 exons of PTEN. Probe pairs were designed according to the manufacturer guidelines, and obtained from IDT (Coralville, USA). Probe pairs were combined into a probe-mix with a concentration of 4nM per oligonucleotide (Table S1). Reactions were carried out in a model 2320 thermocycler (Applied Biosystems, Bleiswijk, the Netherlands). MLPA fragment analysis was performed using GeneMarker V1.85 (Softgenetics, State College, PA, USA).

Breakpoint Mapping and PCR-based screening To map breakpoints for PTEN exons 2-3 and 4-5 deletions, multiple forward or reverse primers were designed for PTEN introns 1, 3 and 5 (Table S2 and S3). Specific primers (Table S4) were selected for PTEN exon 2-3 and 4-5 breakpoint screening. Positive reactions were directly sequenced for both strands or following cloning into the TOPO TA vector (Life Technologies). Sequence reactions were run on the 3130x capillary sequencer (Applied Biosystems) and analyzed using CLC Main Workbench software (Aarhus, Denmark).

Computational detection of putative RAG recombination signal sequences The human PTEN gene (ENSG00000171862) was screened for the presence of cryptic RAG recombination signal sequences (cRSS) using the PERL software algorithms developed by Cowell et al.28 Mouse 12- and 23-RSSs (n=356) from all TCR and Ig loci were used for modeling. Both the program and 12-/23-RSSs were available at http://www.duke.edu/~lgcowell/. The program computes RSS information content (RIC) scores for 12-spacer RSSs (i.e. 28-bp sequences comprising the heptamer, a 12-bp spacer and the nonamer sequences: RIC12) or 23-spacer RSSs (i.e. 39-bp sequences comprising the heptamer, a 23-bp spacer and the nonamer sequences: RIC23) to predict cryptic RSSs (cRSS) based on a comparison to the RIC score for actual immunoglobulin and T-cell receptor RSS. RSSs that potentially function within the range of actual antigen receptor loci RSSs are predicted to score above the -38,81 as RIC12 and -58,45 as RIC23 thresholds. RSSs that potentially function within the range predicted for degenerated RSSs, i.e the so-called cryptic RSSs (cRSS), score below these thresholds but above the background (e.g. the mean of RIC scores determined for non-RSS DNA sequences: -60,07 for RIC12 and -77,76 for RIC23) as previously described.1

Generation of GFPi-PTEN cRSS reporter constructs and recombination assay To measure efficiencies of predicted cRSSs in mediating recombination of GFPi-mRFP reporter constructs, PCR amplified PTEN cRSS1-4 or defined RSS control sequences were cloned into this reporter construct and recombination assays were carried out as described,2 with minor adaptations (see Supplementary Materials and Methods). Predicted PTEN cRSS1-4 or control RSSs were tested in combination with a consensus 12 or 23 RSS (Figure 3B). All RSS or cRSS used in the GFPi-mRFP constructs are summarized in Table S5. Briefly, HEK293T cells grown in standard culture conditions were seeded at 2x105 cells per well in 12-well plates. After 24h, cells were transfected with a transfection mix that contains 5 µL of Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA), 300 µL of Optimem (Gibco, Life Technologies, Paisley, UK), 0.8 µg of CMV-RAG1,2 0.7 µg of CMV-RAG2,2 and 2.5 µg of a GFPi-mRFP construct variant. Medium was replaced 16h after transfection. Cells were harvested at 48h following transfection, and analyzed by flow

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cytometry for GFP and RFP fluorescence using a FACSAria III (BD Bioscience, Madrid, Spain). Data analysis was performed using the FlowJo software (TreeStar Inc., Ashland, OR).

Xenotransplantation of primary T-ALL patient material NOD-SCID-IL2Rγnull (NSG) mice were bred and housed under specific pathogen free (SPF) conditions at the Experimental Animal Facility of ErasmusMC. All experiments have been approved by the committee on animal welfare of ErasmusMC and are in compliance with Dutch legislation. Sets of 6–10 week old mice were intravenously injected with 1-5x106 human bone marrow leukemic cells. Upon signs of illness, mice were euthanized and leukemic cells were collected from the different organs. Likewise, primary transplanted leukemic cells from bone marrow, thymus or spleen were retransplanted.

References

1. Cowell LG, Davila M, Kepler TB, Kelsoe G. Identification and utilization of arbitrary correlations in models of recombination signal sequences. Genome Biol. 2002;3:RESEARCH0072.

2. Trancoso I, Bonnet M, Gardner R, et al. A Novel Quantitative Fluorescent Reporter Assay for RAG Targets and RAG Activity. Front Immunol. 2013;4:110.

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Supplementary Tables

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153

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Chapter 4 Supplementary Protocols: virus production and concentration, viral RNA isolation and RT-qPCR, and human CD34+ HSC transduction.

Reagents X-tremeGENE HP DNA Transfection Reagent: Roche Cat. #06 366 236 001 Opti-MEM I with Glutamax: Life Technologies Cat. #51985-026 0.45 µm filter: Minisart Cat. #17598 or equivalent IVSS VIVASPIN 20 centrifugation concentration column: Sartorius AG, Sigma-Aldrich Cat. #Z614653- 48EA Retronectin: r-Fibronectin CH-296: TaKaRa Cat. #T100A Non-tissue culture treated plates: Falcon (96 wp) Cat. #351172 X-VIVO 10 medium: Lonza Cat. #BE04-743Q Recombinant human stem cell factor (rhSCF): R&D Cat. #255-SC Recombinant human thrombopoietin (rhTPO): R&D Cat. #288-TP/CF Recombinant human FLT3 ligand (rhFlt3L): Miltenyi Biotec Cat. #130-093-855 Protamine Sulfate salt from Salmon grade X: Sigma Cat. #P4020-1G

Virus production and concentration (biosafety level 2) 1) For a 14 cm dish, plate ~8x106 HEK293T cells in 15 ml DMEM + 10% FCS and grow O/N to near confluency 2) Mix lentiviral vector DNA (25 µg), VSV-G (3.7 µg), RRE (GAG/POL; 5 µg), REV (3.1 µg) and serum- free Opti-MEM (4 ml) 3) Add X-tremeGENE HP DNA Transfection Reagent (37 µl) to the DNA mix 4) Incubate at room temperature for 15 minutes 5) In a drop-wise manner: add the transfection mixture to the cells 6) Incubate cells at 37ºC, 5%CO2 for 16-20 hours 7) Remove DMEM, wash cells once with pre-warmed PBS, and add 11 ml of pre-warmed serum-free Opti-MEM 8) Incubate cells at 37ºC, 5%CO2 for 24 hours 9) Collect the first batch of virus-containing medium and pass through a 0.45 µm filter 10) Concentrate roughly 40-fold (for example: 2 plates: concentrate 20 ml to 0.5 ml) using a centrifugation concentration column according to the manufacturer’s recommendations and keep at 4ºC 11) Add 11 ml of fresh pre-warmed serum-free Opti-MEM to the cells and incubate at 37ºC, 5%CO2 for another 24 hours 12) Collect the second batch of virus-medium and repeat steps 9 and 10 13) Combine both batches and determine the concentration factor 14) Take 2 µl of the concentrated virus for viral RNA isolation in step 17 15) Aliquot virus and use fresh or store at -80ºC

Viral RNA isolation and RT-qPCR 16) To determine the number of viral particles by PCR quantification, make a serial dilution of a viral vector DNA plasmid with a known concentration 17) Trizol isolation of viral RNA is performed according to the manufacturer’s procedure 18) RT-qPCR is performed using primers flanking the cPPT region: 5’- AGGTGGAGAGAGAGACAGAGAC-3’ and 5’-CTCTGCTGTCCCTGTAATAAAC-3’

Retronectin-coated plates 19) Coat retronectin at a concentration of 50 µg/ml in PBS in non-tissue culture treated plates at 4ºC (overnight) 20) Before using retronectin-coated plate: remove retronectin, add 2%BSA/PBS, incubate at 37ºC for 30 minutes, and wash twice with PBS

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Human CD34+ HSC O/N stimulation 21) Resuspend HSCs to concentration of 0.5x106/ml in serum-free X-VIVO 10 supplemented with 50 ng/ml rhSCF, 20 ng/ml rhTPO and 50 ng/ml rhFlt3L 22) Incubate the cells at 37ºC, 5%CO2 for 16-20 hours

Human CD34+ HSC transduction (96 well-plate) (biosafety level 2) 23) Collect HSCs and centrifuge to adjust to a concentration of 1x106/ml, keeping them in the same medium with cytokines 24) Add protamine sulfate to the cells to a final concentration of 4 µg/ml 25) Pipet concentrated virus into retronectin-coated (96-well) plate 26) Add HSCs on top of the virus to a final volume of 200 µl/well 27) Mix by gently tapping the plate 28) Spinoculation: centrifuge at 1800 rpm, 32ºC for 1 hour 29) Incubate the transduced cells at 37ºC, 5%CO2 for 24 hours before further use

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Chapter 5 Supplementary Figures

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Supplemental Tables Table S1: Gene Symbols in each of the 16 Gene Signatures from Figure 1C-E

Gene Gene Signature Gene Symbol Signature Gene Symbol 1 JHDM1D 9 S100A8 1 BTG2 9 S100A9 1 NBEA 9 DEFA1///DEFA1B///DEFA3 1 PFKFB2 9 TACSTD2 1 HIST1H2AC 9 CHI3L1 1 ARID5B 9 CD24 1 PELI2 9 CSGALNACT1 1 ERN1 9 MS4A3 1 BTBD11 9 CSTA 1 ZNF704 9 ACTN1 1 RORA 9 PRG2 1 MAFB 9 CLC 1 LOC283070 9 RNASE3 1 CAMK1D///LOC283070 9 S100P 1 CAMK1D 9 RNASE2 1 SPOCK2 9 CTSG 1 DDIT4 9 ELANE 1 TP53INP1 9 CEACAM6 1 HIST2H2BE 9 CST7 1 CREBRF 9 PRTN3 1 AMY1A///AMY1B///AMY1C///AMY2A 9 AZU1 1 LOC728392 9 CEACAM8 1 DCHS1 9 BPI 1 PCMTD1 9 DEFA4 1 BRWD1 9 SLPI 2 TRERF1 9 ANXA3 2 DNTT 9 ETV5 2 ITGB2-AS1 9 TNFRSF4 2 LINC00340 9 TNFRSF18 2 GAL3ST4 9 PRF1 2 OTTHUMG00000166495///RP11-728F11.1 9 VPREB1 2 MBD5 9 CTSW 2 LBH 9 SVOPL 2 SCN3A 9 GPA33 2 IL10RA 9 PPIF 2 FAIM3 9 ELOVL6 2 CRIP1 9 CTHRC1 2 OTTHUMG00000178878///RP11-214C8.5 9 CSRP2 2 PCDH9 9 TNFSF4 2 NEGR1 9 PRR5L 2 ETS1 9 GCNT1 2 BACH2 9 RAB27B 2 PGM2L1 9 GZMA 2 DUSP16 9 NKG7 2 TANC1 9 DNMT3B 2 ITGA6 9 TESC 2 ARRDC4 9 ARRB1 2 AGBL3 9 APOBEC3B 2 PRSS2///PRSS3 9 IGLL1 2 SCN7A 9 GUSBP11///IGLL1///IGLL3P 2 PDE4D 9 COL5A2 2 TUBB2A 9 SLC35F3 2 ARHGAP29 9 CISH 2 SIPA1L2 9 CFH///CFHR1 2 ID2///ID2B 9 TFEC 2 CDKN2D 9 SLC39A14 2 S1PR1 9 NLN 2 IKZF3 9 FKBP4 2 RBMS3 9 TTLL12 2 NSG1 9 PUS7 2 NLGN4X 9 OSM 2 LRRN3 9 H19///MIR675 2 CD8A 9 LIN28B 2 NINL 10 AGR2 2 CD8B///LOC100996919 10 MS4A1 2 EFNB2 10 KLRD1 160

2 VCAN 10 IL18RAP 2 LOC100287598 10 GNLY 2 ID1 10 XCL1 2 RASSF6 10 ITGAM 2 SEC31B 10 SAMD3 2 COX6A1 10 KLRB1 2 GBP1 10 KLRC4-KLRK1///KLRK1 2 KANSL1-AS1 10 CRTAM 2 GAS2 10 PTGDR 2 PALLD 10 PDE7B 2 HNRNPLL 10 EPHA4 2 ANKRD36BP2///LOC101060554 10 KLRC3 2 PLEKHG1 10 MAF 2 NCALD 10 KLRC1///KLRC2 2 SYNM 10 IL2RB 2 GRK5 11 FCRL4 2 RDH10 11 SIGLEC6 2 ALOX5 11 UGCG 2 FHIT 11 CTA-250D10.23///OTTHUMG00000172730 2 TRERF1 11 C1orf186///LOC100505650 2 DNTT 11 TBC1D9 2 ITGB2-AS1 11 CDH1 2 LINC00340 11 FGL2 2 GAL3ST4 11 ENTPD1 2 OTTHUMG00000166495///RP11-728F11.1 11 MPEG1 2 MBD5 11 FGD2 2 LBH 11 LGMN 2 SCN3A 11 CACNA2D3 2 IL10RA 11 SLAMF7 2 FAIM3 11 LY9 2 CRIP1 11 PTPRJ 2 OTTHUMG00000178878///RP11-214C8.5 11 MCOLN2 2 PCDH9 11 CCR2 2 NEGR1 11 TGFBI 2 ETS1 11 NAPSB 2 BACH2 11 MYCL1 2 PGM2L1 11 TLR10 2 DUSP16 11 C5orf20///TIFAB 2 TANC1 11 TIFAB 2 ITGA6 11 LYZ 2 ARRDC4 11 IL6R 2 AGBL3 11 MRC1 2 PRSS2///PRSS3 11 MNDA 2 SCN7A 11 P2RY14 2 PDE4D 11 TYROBP 2 TUBB2A 11 FCER1G 2 ARHGAP29 11 CCR1 2 SIPA1L2 11 CD36 2 ID2///ID2B 11 CTSZ 2 CDKN2D 11 MS4A7 2 S1PR1 11 CD86 2 IKZF3 11 NCF2 2 RBMS3 11 GSN 2 NSG1 11 AMICA1 2 NLGN4X 11 SLC41A2 2 LRRN3 11 DNASE1L3 2 CD8A 11 TICAM2///TMED7-TICAM2 2 NINL 11 GAS2L3 2 CD8B///LOC100996919 11 RGS18 2 EFNB2 11 IDH3A 2 VCAN 11 CD48 2 LOC100287598 11 IGJ 2 ID1 11 TRIB2 2 RASSF6 11 SMIM14 2 SEC31B 11 RNASE6 2 COX6A1 11 CLCN5 2 GBP1 11 GPR18 2 KANSL1-AS1 11 LOC158402 2 GAS2 11 FGR 2 PALLD 11 EPHB1 2 HNRNPLL 11 OTTHUMG00000175583///RP11-4O1.2 2 ANKRD36BP2///LOC101060554 11 ARL4C 161

2 PLEKHG1 11 GPR183 2 NCALD 11 CLECL1 2 SYNM 11 FAM105A 2 GRK5 11 SAMHD1 2 RDH10 11 CLEC4A 2 ALOX5 11 ITGB7 2 FHIT 11 BLNK 3 DENND1B 11 STX7 3 PAPD4 11 IFI44L 3 U2SURP 11 OAS1 3 SF1 11 CRYBG3 3 SREK1 11 ABHD6 3 LOC100996467 11 KYNU 3 MALAT1 11 CCR6 3 CLK4 11 HCK 3 OTTHUMG00000168768///RP11-463O12.5 11 GAS6 3 KMT2A 11 IGK///IGKC 3 CAPZA1 11 DAB2 3 ZMYM2 11 MS4A4A 3 CEP95 11 HDAC9 3 B2M 11 CLEC12A 3 IFNGR1 11 KMO 3 EIF5///SNORA28 11 IRF8 3 MORF4L2 11 CYBB 3 NFAT5 11 DTX4 3 C10orf118 11 ADRB2 3 PCNX 11 THBD 3 AC092620.2///OTTHUMG00000153633 11 RBM47 3 TGFBR1 11 ATP10D 3 INPP5D 11 CD180 3 MIR612///NEAT1 11 GPR114 3 WDR11 11 GAPT 3 OTTHUMG00000168867///RP11-305O6.3 11 PAX5 3 LRRFIP1 11 ARHGAP18 3 CELF2 11 KIAA0930 3 LONRF1 11 SUCNR1 3 ANKRD28 11 LY86 3 ZNF274 12 GZMB 3 LOC100506312 12 NME8 3 SEC61B 12 TCL1A 3 NKTR 12 CMKLR1 3 PIK3C2A 12 PTPRS 3 RAB18 12 IL18R1 3 GNAS 12 SOGA2 3 PELI1 12 TPM2 3 UBE2B 12 CYAT1///IGLC1///IGLV1-44 3 TBRG1 12 IGLC1 3 LINC00597 12 LAMP5 3 SAMD4B 12 LOC285972 3 DAPK1-IT1 12 SEL1L3 3 PGAP1 12 IL3RA 3 MLLT3 12 GNG7 3 SPATA13 12 LHFPL2 3 RGCC 12 ZDHHC14 3 BCL6 12 BANK1 3 KLF4 12 CCDC50 3 RGS1 12 FZD3 3 LOC100507577///LONP2 12 FCRLA 3 ZBTB20 12 IFNLR1 3 OTTHUMG00000175755///RP11-553L6.5 12 SPIB 3 LOC100131564 12 IRF7 3 PPP2R5C 12 FAM129C 3 HECA 12 SULF2 3 LOC100996653 12 LILRA4 3 APBB1IP 12 CLEC4C 3 TNFAIP3 12 PAPLN 3 STK4 13 CDH17 3 PRO2852 13 IL1R2 3 ZFAND6 13 IGSF6 3 ZNF638///ZNF638-IT1 13 CLEC10A 3 RBM5 13 LGALS2 3 RUNX1-IT1 13 HLA-DQB1 162

3 CCAR1 13 HLA-DQA1 3 GGNBP2 13 AGPAT9 3 PDCD6 13 CLIC2 3 RPS27 13 CLEC7A 3 RPS16P5 13 IL13RA1 3 TLE4 13 CST3 3 LOC100996866 13 AXL 3 IKZF1 13 OTTHUMG00000173465///RP11-389C8.2 3 GUSBP3///GUSBP9///LOC100653061///LOC101060519 13 MS4A6A 3 MSI2 13 GEM 3 RHOH 13 PKIB 3 LOC100190986///LOC101060564 13 S100B 3 LINC00342 13 TLR7 3 FLJ31306 13 SERPINF1 3 FLJ38379 13 CCR5 3 TCF12 13 JAZF1 3 RAB11FIP3 13 MARCH1 3 ZNF124 13 CPVL 4 C1orf54 13 IDO1 4 GGTA1P 13 FCGR2B 4 FCER1A 14 TASP1 4 LGALS3 14 GXYLT2 4 HLA-DRB1///HLA-DRB3///HLA-DRB4///HLA-DRB5///LOC100507709///LOC100507714 14 TRDC 4 SERPINB2 14 YME1L1 4 THEMIS2 14 CD3D 4 SETBP1 14 TRAT1 4 SERPINB9 14 GPR171 4 EMP1 14 GPR65 4 HLA-DPA1 14 LPXN 4 CD74 14 CDK2AP2 4 CSF2RB 14 VANGL1 4 HLA-DOA 14 VASH2 4 AHNAK 14 CD7 4 HLA-DRB1///HLA-DRB3///HLA-DRB4///HLA-DRB5///LOC100507709///LOC100507714///LOC101060779 14 HES4 4 DUSP1 14 ICOS 4 RGS2 14 MYO1B 4 KLF6 14 CEP41 4 SIK1 14 SLC35B3 4 ARRDC3 14 TMEM251 4 ZBTB24 14 ATG4C 4 PLK2 14 VPS41 4 KLF2 14 ATP6V1D 4 PPP1R15A 14 BPNT1 4 DUSP4 14 KIAA1841 4 ETNK1 14 PPHLN1 4 EGR3 14 C17orf62 4 PTGS2 14 PWWP2A 4 NR4A2 14 CBR4 4 FOSB 14 WDR67 4 FOS 14 DCAF12 4 VIM 14 LRIF1 4 SGK1 14 ST8SIA4 4 RASGEF1B 14 DNA2 4 LOC284454 14 LOC389831 4 ANXA1 14 HES1 4 ITPKB 14 GIMAP7 4 NASP 14 IL17RA 4 EGR2 14 DPP4 4 LAIR2 14 IFNAR1 5 VWF 14 PAQR8 5 EFEMP1 14 TRIM13 5 MGP 14 ACSL3 5 MAFIP 14 SHCBP1 5 ELL2 14 UMODL1 5 FN1 14 PHF20L1 5 HBEGF 14 STK38L 5 ABLIM1 14 CEP19 5 NRIP1 14 C9orf64 5 PIK3R1 14 HYLS1 5 MYH10 14 ATP6V1C1 5 OTTHUMG00000182869///RP11-420K14.2 14 ERCC4 5 FLJ13197 14 ARL13B 163

5 ZBTB16 14 KBTBD7 5 CDKN1C 14 HDHD1 5 PXDN 14 FBXO5 5 HIST3H2A 14 NCAPG2 5 OCLN 14 SKA3 5 HLA-DRB4 14 CCNF 5 XIST 14 TROAP 5 FOXO3///FOXO3B 14 AURKB 5 MXI1 14 CASC5 5 DGKE 14 NUSAP1 5 PPP1R3B 14 TMPO 5 ABCG1 14 CCDC15 5 RBM38 14 SGOL2 5 SHISA2 14 GTSE1 5 GNL1 14 CDCA2 5 BAG3 14 E2F7 5 SPTBN1 14 E2F8 5 CREM 14 KIF18B 5 KBTBD2 14 MKI67 5 IL6ST 14 KIF23 5 SKIL 14 ECT2 5 RIT1 14 CENPF 5 HIST2H2AA3///HIST2H2AA4 14 NUF2 5 PIK3IP1 14 FANCI 5 ANKDD1A 14 KIF15 5 CCNL1 14 KIF18A 5 JUN 14 ESPL1 5 MEX3C 14 KIFC1 5 CSRNP1 14 BUB1B 5 BCL10 14 STIL 5 EPC1 14 KIF14 5 BTG1 14 CENPE 5 KRAS 14 DEPDC1 5 CHMP3///RNF103///RNF103-CHMP3 14 KIF2C 5 KLHL24 14 HJURP 5 MIR4800///MXD4 14 CDCA8 5 OTUD1 14 CCNB2 5 CA2 14 CENPA 5 RIN2 14 NEK2 5 LILRB2 14 KIF11 5 CPQ 14 KIF4A 5 ADAM8 14 BUB1 5 SQSTM1 14 RRM2 5 IL8 14 TPX2 5 TRIB1 14 RACGAP1 5 CCL3///CCL3L1///CCL3L3///LOC101060267 14 MELK 5 ZFP36 14 SPAG5 5 TSC22D3 14 NCAPG 5 METRNL 14 SRSF1 5 ATF3 14 NDC80 5 NAMPT 14 SPDL1 5 RC3H2 14 CENPN 5 MAFF 14 C9orf40 5 POLB 14 CKAP2L 5 CUL1 14 ARHGAP11A 5 HEMGN 14 FOXM1 5 PER1 14 NCAPH 5 PCIF1 14 RAD54L 5 CSNK1E///LOC100996460 14 BIRC5 5 CUL4A 14 CCDC167 5 STAT3 14 CCNE2 5 FAM3C 14 ERCC6L 5 RAPGEF2 14 PARPBP 5 RNF130 14 C11orf82 5 MECP2 14 KNSTRN 5 SRSF11 14 CDCA5 5 NLRP1 14 ASF1B 5 MED23 14 AURKA 5 ST3GAL1 14 CDKN3 5 ZNF638 14 MND1 5 IREB2 14 NIF3L1 5 MIR22///MIR22HG 14 MTFR2 164

5 PPP1R16B 14 TCF19 5 FLJ43663 14 HIRIP3 5 ISG20 14 SPC24 5 PIM2 14 RTP4 5 PDE4B 14 RBL1 5 SOCS3 14 TOP2A 5 LPGAT1 14 FAM72A///FAM72B///FAM72C///LOC101060656 5 HLA-E 14 UHRF1 5 SMAD3 14 WDR76 5 RNF144B 14 INTS7 5 IDS 14 CDK1 5 ERO1LB 14 DLGAP5 5 ACVR2A 14 TTK 5 TMEM200A 14 UBE2C 5 IRAK2 14 CDCA3 5 GABARAPL1 14 KIF20A 5 SLC2A3 14 CEP55 5 SYTL3 14 HMMR 6 HAS1 14 ANLN 6 CTSL1 14 BRIP1 6 NINJ1 14 ERI2 6 CXCL3 14 DIAPH3 6 TM4SF1 14 C1orf112 6 INHBA 14 SMC2 6 CXCL1 14 DEPDC1B 6 AQP9 14 UBE2T 6 CCL2 14 CCNA2 6 ICAM1 14 CDC20 6 PHLDA1 14 CCNB1 6 TNFRSF9 14 OIP5 6 IL6 14 SPC25 6 ZNF709 14 PKP4 6 C10orf10 14 BRCC3 6 RHOB 14 CDC25A 6 IER3 14 FBXO22 6 C15orf48 14 SLC25A19 6 BCL2A1 14 POLR1B 6 TLN1 14 IDH1 6 HIST1H2BC 14 HK2 6 TPP2 14 GIMAP6 6 IL1RL1 14 GIMAP8 6 CNST 14 GIMAP1 6 USP36 14 NABP1 6 MAX 14 NRARP 6 FTH1 14 GAS1 6 USP15 14 DCPS 6 GFOD1 14 ARL11 6 DOCK7 14 SGK223 6 AREG///AREGB 14 STAMBPL1 6 C7orf73///SLC13A4 14 OPN3 6 SLC8A3 14 NLRP2 6 TPM3 14 PSPH 6 SOD2 14 FAM173B 6 GABPB1 14 CHEK2 6 STX11 14 ICMT 6 CXCL2 14 POLR3K 6 FAM198B 14 C2orf43 6 CYLD 14 RAD51C 6 RELB 14 GART 6 BCL3 14 CSE1L 6 NBL1 14 OTTHUMG00000159786///RP11-85F14.5 6 SLCO3A1 14 TRMT1L 6 SMURF1 14 METTL13 6 SEMA6A 14 UBE2L3 6 SDK2 14 EPDR1 6 MINOS1-NBL1///NBL1 14 RPE 6 SERAC1 14 BRI3BP 6 EIF4G3 14 RMI1 6 NFKB2 14 KIAA1524 6 USP53 14 GINS1 6 ERMN 14 ARFIP1 6 TCF25 14 SHQ1 165

6 GNG4 14 TARP///TRGC2 6 KSR1 14 RAD51 6 SKI 14 DHFR 6 MEG3 14 BRCA1 6 AVP 14 MCM8 6 THBS1 14 TK1 6 HBA1///HBA2 14 GUCY1B3 6 NR4A3 14 TAF5 6 BMP6 14 TDP1 6 CDC42 14 TUBG1 6 LOC100507672 14 PAGR1 6 BEX5 14 XRCC4 6 CDC14B 14 SRSF10 6 NEDD4L 14 CENPM 6 HBG1///HBG2 14 WDHD1 6 DLK1 14 PANK1 6 PPBP 14 LCMT2 6 G0S2 14 PSAT1 6 TMEM163 14 EXO1 6 TNIK 14 C12orf4 6 THSD7A 14 C18orf54 6 LIMS3///LIMS3L 14 MED11 6 ARHGEF12 14 EXOSC3 6 FOXO1 14 GEMIN6 6 FAM65C 14 UMPS 6 PDZD2 14 TRIP13 6 EREG 14 CDC6 6 HBB 14 MCM10 6 LOC254057 14 CHAC2 6 PEAR1 14 CLUAP1 6 FCHO2 14 RAD54B 6 CRHBP 14 SAPCD2 6 MYCT1 14 GINS4 6 DNAJC6 15 CD1C 6 BEX2 15 SMPD3 6 ACSL1 15 CD1E 6 BEX1 15 HHIP-AS1 6 H1F0 15 CHI3L2 6 KLF3 15 SYNE2 6 LRP5L 15 TNRC6C 6 PRKACB 15 ARPP21 6 NLK 15 LOC100130503 6 C7orf41 15 DOCK9 6 MAGI2-AS3 15 OTTHUMG00000176181///RP11-119F7.5 6 OTTHUMG00000176268///RP11-18F14.2 15 CCR9 6 SLC35D2 15 GSDMB 6 LOC285812 15 CTC1 6 PTX3 15 HS3ST3B1 6 GBP2 15 ZBTB1 6 ABCB1 15 FAM69A 6 CEBPB 15 RUFY3 6 FGD5 15 CCNG2 6 ABHD17C 15 PVRIG 6 MPP7 15 NOTCH3 6 IRS2 15 HDAC4 6 PPM1H 15 LRRC28 6 PLOD2 15 ACPL2 6 RBPMS 15 LINC00226 6 HLF 15 CR2 6 PBX1 15 ZC3H6 6 CALN1 15 NKD2 6 GPR126 15 GLRB 6 GNAI1 15 OTTHUMG00000017520///RP11-144L1.4 6 ALDH1A1 15 CX3CR1 6 TNFRSF10A 15 LOC101060391 6 GATA2 15 CCR8 6 TNFRSF10D 15 TMSB15A///TMSB15B 6 CNRIP1 15 SLC16A10 6 ITGA9 15 CD1B 6 TAL1 15 RAG2 6 TCF7L2 15 AQP3 6 DACH1 15 CD1A 166

6 LRP6 15 GRAP2 6 ZNF521 15 CD3E 6 NPR3 15 SLC7A3 7 SOCS2 15 RAPGEF5 7 ARMCX1 15 ATP6AP1L///FLJ41309 7 MEST 15 SRPK2 7 CXCR7 15 TRBC1 7 HSPA1A///HSPA1B 15 TRBC2 7 C11orf96 15 MAL 7 GSTM3 15 ID3 7 FHL1 15 UBASH3A 7 SNTB1 15 TLR5 7 BSPRY 15 TSHR 7 EPCAM 15 IRS1 7 HBD 15 HHIP 7 UCHL1 15 PTCRA 7 NRIP3 15 SLC8A1-AS1 7 PDE4DIP 15 PAFAH2 7 MATR3///SNHG4 15 RAG1 7 AC005682.5///OTTHUMG00000152563 15 CD1D 7 TMEM220 15 MME 7 GYPC 15 PLCL1 7 FAM171B 15 SH3TC1 7 ADM 15 SPRED2 7 SMIM3 15 BCL2L11 7 CRYGD 15 FOXO6///FOXO6 7 RIPK2 15 CYP2U1 7 ZBTB8A 15 PPP1R1C 7 GBP4 15 LOC79015 7 DRAM1 15 STAG3 7 MPL 15 CRNDE 7 ICA1 15 ADAMTS1 7 CMBL 15 WT1 7 SYNGR1 15 LOC283788 7 FAM69B 15 LOC728613 7 TCTEX1D1 15 SLC16A6 7 SDPR 15 IPP 7 OTTHUMG00000020937///RP11-251M1.1 15 TFDP2 7 CPA3 15 SBK1 7 KBTBD11 15 POU2AF1 7 SLC22A15 15 ITK 7 RAB13 15 RASGRP1 7 GRB10 15 CAMK4 7 SLC2A5 15 TCF7 7 ELK3 15 PCDH10 7 PVRL2 15 PTPRK 7 SORL1 15 CD96 7 PROK2 15 GATA3 7 GPR27 15 IL32 7 SNORD3A///SNORD3B-1///SNORD3B-2///SNORD3C///SNORD3D 15 TMOD2 7 PRSS2 15 PLCG1 7 MSRB3 15 NMT2 7 NAP1L3 15 P2RX5 7 TPM1 15 IKZF2 7 HMGA2 15 FAM63B 7 NUDT11 15 DGKA 7 CABLES1 15 RGPD1///RGPD2 7 CKAP4 15 LAT 7 RAMP1 15 BCL11B 7 TFPI 15 ZAP70 7 IL1RAP 15 THEMIS 7 IRAK3 15 CD247 7 GBP5 15 SLFN5 7 FOSL2 15 LEF1 7 SLC22A4 15 CD3G 7 TRH 15 LDLRAD4 7 TTC7B 15 AEBP1 7 HOXB2 15 SLC4A4 7 ME3 15 GALNT2 7 CHRM3 15 KIF3A 7 MECOM 15 PARP8 7 MEIS1 15 RIMS3 167

7 PHTF1 15 FYB 7 LOC100505573 15 YPEL1 7 LATS2 15 KIAA0226L 7 MBOAT7 15 NEDD9 7 LPCAT2 15 CD72 7 AAED1 15 ATM 7 WHAMMP2///WHAMMP3 15 IPCEF1 7 SEMA4C 15 APBA2 7 KIT 15 SERHL2 7 HOPX 15 CD79A 7 HOXA3 15 EBF1 7 HOXA5 15 CDH2 8 C11orf21 16 KBTBD6 8 GSAP 16 FRMD4A 8 GATM 16 DLEU2 8 STAP1 16 NAPEPLD 8 LYN 16 EVL 8 ALCAM 16 LOC100506776 8 VCL 16 SLC4A7 8 MEF2C 16 NREP 8 BTK 16 KATNBL1 8 MGLL 16 AHRR 8 B3GNT7 16 FANCA 8 FAM49A 16 POLQ 8 PLXNB2 16 ZNF519 8 PHACTR1 16 LOC100507312 8 PLEK 16 ASPM 8 BASP1 16 ESCO2 8 IRF5 16 FAM111B 8 DUSP6 16 ATAD2 8 CD300LF 16 RHOU 8 RAB31 16 LYST 8 PIK3AP1 16 LOC284757 8 MYO1F 16 GTF2H2B 8 CTBP2 16 TXLNG2P 8 CDC42BPA 16 DSERG1 8 IPO11///LRRC70 16 PLGLB1///PLGLB2 8 DUSP10 16 SEPT1 8 C1orf186 16 HOMER1 8 RPS11 16 RRN3P3 8 BHLHE40 16 UGP2 8 SPRY1 16 LOC100996511 8 NFIL3 16 MIR181A2HG 8 KLF11 16 GOLGA2P5 8 SPRY2 16 OTTHUMG00000183913///RP11-932O9.10 8 CD69 16 EPM2AIP1 8 EGR1 16 C1orf132 8 DUSP5 16 CD28 8 FXYD5 16 DHFRL1 8 CD44 16 SUV420H1 8 OTTHUMG00000176545///RP11-510J16.3 16 AKAP2///PALM2-AKAP2 8 MAML3 16 TRAF3IP3 8 LAPTM5 16 SATB1 8 CRIM1 16 INPP4A 8 IGHM 16 ITGAL 8 SNX9 16 CDC25B 8 FLT3 16 SH3KBP1 8 HLX 16 NDST3 8 TCF4 16 HNRNPR 8 NFKBIZ 16 ARHGAP19 8 CAPN2 16 GRSF1 8 MAP3K8 16 CEP70 8 ADAM28 16 WDR7 8 MYLIP 16 CEP78 8 KLF9 16 STAU2 8 LILRA2 16 TBCD 8 HPGDS 16 LRRC1 8 CD302///LY75-CD302 16 WBP1L 8 HHEX 16 SELPLG 8 TNS3 16 JPH1 8 SH3RF1 16 PRKCA 8 ANKRD33B 16 BIK 168

8 NLRP3 16 OTTHUMG00000178927///RP11-196G18.23 8 GNG11 16 CHST2 8 ARSD 16 STS 8 FNDC3B 16 RHBDD1 8 SLC25A13 16 OXNAD1 8 GOLIM4 16 MNS1 8 TIMP1 16 PSRC1 8 NEK3 16 CDC25C 8 C10orf54 16 LOC100288637 8 DUSP3 16 FBXO43 8 TNFRSF1B 16 MYBL1 8 IL18 16 LEF1-AS1 8 ITPRIPL2 16 NEIL3 8 CEBPD 16 LOC100506100 8 FAM46A 16 KIAA0922 8 CTNNA1 16 MTA3 8 LMO2 16 CEP128 8 TNFRSF10B 16 ITPR2 8 CFD 16 TCFL5 8 SLC7A5 16 STK11IP 8 BATF 16 ZNF280D 8 LOC729680 16 APOLD1 8 EIF4EBP1 16 ZEB1-AS1 8 TRIM6 16 CD2 8 MGST1 16 LOC100507600 8 MPO 16 CAPSL 8 SLC22A16 16 MGAT4A 8 FAH 16 NDFIP2 8 BCAT1 16 HIVEP3 8 MTHFD1L 16 PCSK5 8 TNFAIP2 16 IGHG1 8 TNFSF13B 16 IGH///IGHA1///IGHA2///IGHD///IGHG1///IGHG3///IGHG4///IGHM///IGHV3-23///IGHV4-31 8 CD33 16 MAP1A 8 PLA2G4A 16 RCAN1 8 MOB3B 16 SH2D1A 8 KCNK17 16 PBK 8 GCSAML 16 IL7R 8 NFE2 16 ADA 8 HSH2D 16 LCK 8 LAT2 16 LIG4 8 P2RY2 16 CDKN2C 8 SIGLEC17P 16 E2F2 8 ACY3 16 NETO2 8 S100Z 16 GAS7 8 PAM 16 FXYD2 8 CD34 16 IGF2R 8 C11orf74 8 PYGL 8 CLDN10 8 CYB561 8 CLEC11A 8 MYCN 8 SPON1 8 ANGPT1 8 PTPRD 8 SPINK2 8 KIAA0125 8 EFHC2 8 MCTP2 8 AP001171.1///OTTHUMG00000074606 8 ERG 8 IGFBP7 8 ARHGEF40 8 MYLK 8 LYL1 8 PRAM1 8 SERPINB1 8 ATP8B4 8 XBP1 8 LAMC1 8 C19orf77 8 MAP7

169

8 PROSER2 8 HTR1F 8 SPARC 8 SHANK3 8 F2RL1 8 ZC3H12C 8 CNKSR3 8 MN1 8 TUSC1 8 C1QTNF4 8 C9orf43 8 KIF13A 8 BAALC 8 PROM1 8 IQCJ-SCHIP1///SCHIP1 8 IL1B 8 B3GNT5 8 NIPAL2 8 DEPTOR 8 STOM 8 SERPINB6 8 CREG1 8 CD63 8 C17orf58 8 MYO5C 8 RAB3D 8 ASB9 8 SETD9 8 CYTL1 8 DPPA4 8 KCTD15 8 PDGFC 8 MATR3 8 CCND2 8 CPXM1 8 SLC39A8 8 SERPINE2 8 LAPTM4B 8 KIAA1211 8 STON2 8 LDLRAD3 8 KCNQ5 8 SLC27A2 8 KIF9 8 SLC35F2

170

Table S2: Gene set enrichment on gene expression of murine T-cell development, Figure 2B

Gene Signatures #7 and #8 Gene Signatures #2, #15 and #16 Gene Symbol Rank in gene list Running ES Core enrichment Gene Symbol Rank in gene list Running ES Core enrichment RAB31 0 0.023 Yes CD72 21 0.021 No PLEK 2 0.043 Yes PARP8 369 0.016 No HHEX 4 0.061 Yes TFDP2 399 0.026 No KIT 9 0.079 Yes ACPL2 587 0.027 No LYN 10 0.096 Yes SLC4A7 818 0.025 No MEF2C 12 0.113 Yes PAFAH2 851 0.032 No BTK 14 0.131 Yes STAU2 1238 0.022 No LYL1 15 0.147 Yes KIF3A 1913 -0.002 No ANGPT1 19 0.164 Yes ARRDC4 1914 0.003 No CD34 28 0.179 Yes CAPSL 2025 0.004 No PLA2G4A 32 0.194 Yes FAM69A 2152 0.004 No KIF13A 45 0.207 Yes SH3TC1 2312 0.002 No PLXNB2 54 0.220 Yes FRMD4A 2418 0.002 No ERG 63 0.233 Yes SLC16A6 2503 0.003 No MYO1F 69 0.245 Yes SYNE2 2504 0.007 No PYGL 72 0.257 Yes EBF1 3291 -0.023 No CTBP2 82 0.269 Yes STK11IP 3359 -0.023 No B3GNT5 99 0.280 Yes ALOX5 3451 -0.023 No CABLES1 101 0.291 Yes RASSF6 3470 -0.021 No IRF5 103 0.303 Yes NMT2 3613 -0.024 No CD44 108 0.314 Yes IL10RA 3730 -0.026 No PIK3AP1 114 0.325 Yes CEP70 3799 -0.025 No VCL 126 0.336 Yes PGM2L1 3814 -0.023 No MYCN 133 0.346 Yes UGP2 4240 -0.039 No MPO 142 0.357 Yes GRK5 4308 -0.039 No ALCAM 148 0.367 Yes ATM 4523 -0.046 No NFE2 171 0.377 Yes RUFY3 4632 -0.049 No TCF4 284 0.381 Yes DUSP16 4635 -0.047 No SNX9 290 0.389 Yes CD79A 4710 -0.048 No ZC3H12C 330 0.396 Yes GRSF1 4822 -0.051 No IL18 335 0.404 Yes DOCK9 4974 -0.055 No HOXA5 344 0.412 Yes HNRNPR 4991 -0.054 No SLC25A13 354 0.420 Yes PLEKHG1 5034 -0.054 No FAM69B 355 0.428 Yes CEP78 5175 -0.058 No CTNNA1 360 0.436 Yes MTA3 5189 -0.057 No FAM49A 370 0.444 Yes INPP4A 5287 -0.060 No TRIM6 386 0.451 Yes ARHGAP29 5378 -0.062 No CNKSR3 392 0.459 Yes PALLD 5420 -0.062 No LMO2 394 0.467 Yes RHBDD1 5450 -0.062 No LAT2 452 0.472 Yes LRRN3 5481 -0.062 No HLX 467 0.479 Yes NINL 5490 -0.061 No P2RY2 501 0.485 Yes CR2 5603 -0.064 No IL1RAP 519 0.491 Yes RAPGEF5 5719 -0.068 No EIF4EBP1 523 0.498 Yes OXNAD1 5783 -0.070 No ICA1 539 0.505 Yes EFNB2 5905 -0.074 No MEIS1 573 0.510 Yes ITGA6 6036 -0.078 No LPCAT2 595 0.516 Yes TBCD 6251 -0.087 No ITPRIPL2 613 0.523 Yes RDH10 6597 -0.101 No HPGDS 628 0.529 Yes JPH1 6862 -0.113 No FLT3 631 0.536 Yes ZC3H6 6869 -0.112 No CKAP4 636 0.542 Yes SLFN5 6938 -0.115 No PAM 649 0.549 Yes ID1 6951 -0.115 No GATM 669 0.555 Yes CRIP1 7203 -0.126 No ARMCX1 717 0.559 Yes FHIT 7332 -0.131 No HMGA2 755 0.564 Yes FAIM3 7477 -0.138 No GSTM3 763 0.570 Yes PSRC1 7753 -0.150 No GOLIM4 779 0.576 Yes BCL2L11 7796 -0.151 No CD33 892 0.577 Yes CHST2 8127 -0.166 No NAP1L3 901 0.582 Yes YPEL1 8448 -0.179 No MAML3 963 0.585 Yes SCN7A 8951 -0.201 No MGST1 1076 0.586 Yes AGBL3 9194 -0.210 No GBP5 1077 0.592 Yes STAG3 9339 -0.216 No SEMA4C 1117 0.595 Yes AHRR 9658 -0.229 No DUSP3 1147 0.599 Yes NDFIP2 9800 -0.234 No MSRB3 1181 0.603 Yes RCAN1 9909 -0.238 No IRAK3 1227 0.607 Yes NDST3 10158 -0.247 No RAB3D 1254 0.611 Yes NSG1 10250 -0.250 No

171

KIF9 1313 0.613 Yes POU2AF1 10391 -0.255 No CD63 1317 0.618 Yes APBA2 10449 -0.256 No FNDC3B 1320 0.623 Yes GAL3ST4 10611 -0.262 No FAM46A 1328 0.628 Yes GAS7 10758 -0.267 No TNFAIP2 1332 0.633 Yes CX3CR1 10866 -0.271 No SPRY2 1338 0.638 Yes S1PR1 11101 -0.279 No TFPI 1404 0.640 Yes ADAMTS1 11356 -0.289 No MPL 1418 0.645 Yes EVL 11375 -0.288 No MECOM 1498 0.646 Yes IKZF2 11638 -0.298 No KLF11 1547 0.648 Yes TSHR 11754 -0.302 No CDC42BPA 1691 0.647 Yes PCSK5 11874 -0.305 No GRB10 1809 0.646 Yes SLC7A3 12006 -0.309 No LATS2 1823 0.650 Yes NETO2 12466 -0.328 No TNS3 1912 0.650 Yes TMOD2 12502 -0.328 No SHANK3 1915 0.654 Yes GLRB 12787 -0.338 No MN1 2041 0.653 Yes SUV420H1 12849 -0.339 No MCTP2 2067 0.656 Yes PLCL1 13625 -0.371 No MAP3K8 2115 0.657 Yes NEIL3 13626 -0.369 No MYO5C 2194 0.658 Yes SYNM 13747 -0.372 No SNTB1 2261 0.659 Yes WT1 14113 -0.386 No NFIL3 2317 0.660 Yes MBD5 14171 -0.387 No FXYD5 2391 0.660 Yes ID2 14366 -0.393 No CPXM1 2433 0.662 Yes PCDH9 14484 -0.396 No DUSP6 2633 0.657 Yes ATAD2 14940 -0.414 No CRIM1 2660 0.659 Yes SCN3A 15277 -0.426 No NFKBIZ 2740 0.659 Yes PCDH10 15373 -0.428 No RPS11 2789 0.660 Yes DLEU2 15620 -0.436 No GYPC 2795 0.663 Yes HHIP 15683 -0.436 No FOSL2 2804 0.666 Yes MME 15763 -0.437 No LAPTM4B 2869 0.666 Yes NAPEPLD 15833 -0.438 No GNG11 2870 0.669 Yes CDH2 15903 -0.438 No MYLK 2886 0.672 Yes CYP2U1 15979 -0.439 No TNFRSF10B 3083 0.666 Yes FANCA 16117 -0.442 No BATF 3145 0.666 Yes VCAN 16153 -0.441 No SDPR 3180 0.668 Yes PBK 16185 -0.440 No UCHL1 3306 0.665 Yes IL7R 16226 -0.439 No SOCS2 3468 0.660 Yes FXYD2 16538 -0.450 No HSH2D 3496 0.662 Yes IPCEF1 16750 -0.457 No CPA3 3506 0.664 Yes AEBP1 16869 -0.459 No FHL1 3516 0.666 Yes SLC4A4 16978 -0.461 No CD69 3519 0.669 Yes POLQ 17262 -0.471 No TPM1 3521 0.671 Yes HOMER1 17373 -0.472 No CAPN2 3524 0.674 Yes ASPM 17379 -0.470 No BHLHE40 3525 0.676 Yes PTPRK 17490 -0.472 No DUSP5 3586 0.676 No PPP1R1C 17543 -0.471 No EFHC2 3800 0.669 No RHOU 17688 -0.474 No NLRP3 3826 0.670 No AQP3 17833 -0.478 No MATR3 3861 0.671 No BIK 17936 -0.479 No BASP1 3921 0.671 No NKD2 17946 -0.476 No ATP8B4 4068 0.666 No LRRC1 17995 -0.476 No NUDT11 4182 0.663 No MAL 18144 -0.479 No SLC22A15 4250 0.662 No MYBL1 18292 -0.482 No MBOAT7 4381 0.658 No FBXO43 18398 -0.484 No DRAM1 4441 0.658 No GALNT2 18506 -0.485 No SLC27A2 4640 0.651 No NEGR1 18581 -0.485 No FAM171B 4755 0.647 No LRRC28 18595 -0.482 No PDGFC 5119 0.633 No P2RX5 18891 -0.492 No RIPK2 5382 0.622 No SMPD3 18946 -0.491 No FAH 5468 0.620 No MGAT4A 19097 -0.494 No CCND2 5470 0.621 No CCNG2 19220 -0.496 No EGR1 5727 0.611 No CCR8 19280 -0.495 No TMEM220 5735 0.611 No ESCO2 19389 -0.497 No SLC39A8 5887 0.605 No RBMS3 19530 -0.499 No HOXB2 5890 0.606 No BACH2 19576 -0.498 No ACY3 5923 0.606 No NEDD9 19624 -0.496 No CEBPD 6050 0.601 No DNTT 19641 -0.493 No TUSC1 6088 0.600 No CDKN2C 19717 -0.493 No CYB561 6104 0.600 No CDC25C 19833 -0.494 No STOM 6658 0.576 No ZBTB1 19978 -0.497 No STAP1 6893 0.566 No PDE4D 20213 -0.503 Yes KCNQ5 7180 0.554 No EPM2AIP1 20231 -0.500 Yes HOPX 7376 0.545 No TANC1 20370 -0.502 Yes

172

ANKRD33B 7426 0.543 No WDR7 20405 -0.499 Yes BSPRY 7520 0.539 No FAM63B 20481 -0.499 Yes MTHFD1L 7705 0.531 No IRS1 20511 -0.496 Yes PHACTR1 7887 0.523 No TCFL5 20559 -0.494 Yes LDLRAD3 7926 0.522 No NOTCH3 20582 -0.491 Yes B3GNT7 8251 0.508 No RIMS3 20758 -0.494 Yes LAMC1 8390 0.502 No COX6A1 20841 -0.493 Yes SPRY1 8581 0.494 No HDAC4 20879 -0.491 Yes SLC22A4 8690 0.490 No SEC31B 21056 -0.494 Yes RAB13 8804 0.486 No APOLD1 21084 -0.490 Yes TNFRSF1B 9005 0.477 No CDC25B 21224 -0.492 Yes SPARC 9169 0.471 No GAS2 21363 -0.493 Yes RAMP1 9170 0.471 No ARHGAP19 21464 -0.493 Yes XBP1 9206 0.470 No NCALD 21545 -0.491 Yes MYLIP 9290 0.467 No HS3ST3B1 21595 -0.488 Yes CHRM3 9431 0.462 No ITGAL 21773 -0.490 Yes CD300LF 9453 0.462 No PTCRA 21814 -0.487 Yes CMBL 9680 0.452 No SEPT9 21820 -0.481 Yes TNFSF13B 9730 0.451 No LYST 21843 -0.477 Yes NIPAL2 9768 0.450 No MNS1 21880 -0.473 Yes IL1B 9901 0.445 No PRKCA 21923 -0.469 Yes ZBTB8A 10028 0.441 No SLC16A10 21982 -0.465 Yes SORL1 10148 0.436 No SPRED2 22073 -0.463 Yes IGFBP7 10154 0.437 No LBH 22103 -0.459 Yes LAPTM5 10199 0.436 No TUBB2A 22278 -0.460 Yes SLC35F2 10335 0.431 No RAG2 22294 -0.454 Yes SERPINE2 10478 0.425 No CD2 22351 -0.449 Yes C1QTNF4 11047 0.402 No ADA 22414 -0.445 Yes SLC7A5 11283 0.392 No E2F2 22506 -0.441 Yes S100Z 11421 0.387 No ARPP21 22519 -0.434 Yes PVRL2 11712 0.376 No SBK1 22543 -0.427 Yes NEK3 12156 0.358 No PLCG1 22551 -0.420 Yes PRSS2 12402 0.348 No CD96 22585 -0.413 Yes STON2 12861 0.329 No TNRC6C 22588 -0.405 Yes SPINK2 13304 0.311 No SELPLG 22617 -0.398 Yes ADM 13365 0.310 No CCR9 22628 -0.390 Yes CXCR7 13385 0.311 No UBASH3A 22639 -0.383 Yes PROM1 13415 0.311 No CD8A 22641 -0.374 Yes CLEC11A 13474 0.310 No TRERF1 22661 -0.367 Yes ADAM28 13623 0.305 No SIPA1L2 22711 -0.360 Yes ASB9 13845 0.297 No DGKA 22740 -0.352 Yes CFD 14062 0.289 No ID3 22806 -0.345 Yes TTC7B 14669 0.264 No CD28 22808 -0.335 Yes CRYGD 14848 0.258 No SATB1 22813 -0.325 Yes PTPRD 15343 0.238 No SH2D1A 22825 -0.316 Yes SLC22A16 15517 0.232 No LIG4 22828 -0.305 Yes BAALC 17146 0.163 No SH3KBP1 22829 -0.295 Yes NRIP3 17299 0.158 No HIVEP3 22835 -0.285 Yes MGLL 17390 0.157 No IKZF3 22863 -0.275 Yes TRH 17667 0.147 No CAMK4 22868 -0.264 Yes KBTBD11 18498 0.113 No GRAP2 22873 -0.253 Yes CYTL1 18655 0.108 No THEMIS 22876 -0.241 Yes PDE4DIP 18952 0.098 No GATA3 22878 -0.230 Yes F2RL1 19071 0.095 No ITK 22887 -0.218 Yes PROK2 19095 0.097 No RASGRP1 22899 -0.206 Yes KCTD15 19117 0.098 No FYB 22904 -0.193 Yes HTR1F 19175 0.098 No TCF7 22907 -0.180 Yes SPON1 19237 0.098 No IGF2R 22910 -0.166 Yes TIMP1 19830 0.075 No LCK 22911 -0.153 Yes SLC2A5 20039 0.069 No ITPR2 22915 -0.139 Yes PRAM1 20423 0.055 No ETS1 22918 -0.125 Yes SYNGR1 20540 0.053 No CD247 22923 -0.110 Yes DPPA4 20676 0.050 No ZAP70 22928 -0.095 Yes ME3 20966 0.041 No BCL11B 22932 -0.080 Yes EPCAM 20977 0.043 No LEF1 22933 -0.064 Yes GPR27 21492 0.025 No LAT 22938 -0.046 Yes TCTEX1D1 21586 0.024 No CD3E 22939 -0.026 Yes PHTF1 21786 0.020 No CD3G 22941 0.000 Yes BCAT1 22231 0.005 No SH3RF1 22848 -0.014 No DUSP10 22869 -0.007 No GBP4 22905 0.002 No

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Table S3: Gene Symbols in Gene Clusters I-V from Figure 2C

Gene Cluster I Gene Cluster II Gene Cluster III Gene Cluster IV Gene Cluster V SNTB1 CRIM1 KATNBL1 RCAN1 IPP DRAM1 CYTL1 OTTHUMG00000176181///RP11-119F7.5 PAFAH2 SLC16A10 IL1RAP TNS3 GRSF1 SPRED2 STAG3 SDPR PROM1 STAU2 GAS2 GSDMB ARHGAP29 BAALC TXLNG2P CX3CR1 CD8A HLX LMO2 POU2AF1 NLGN4X CD8B///LOC100996919 MEIS1 LAPTM4B GATA3 NINL LYST NFIL3 BASP1 BATF SMPD3 IRS1 KIT FNDC3B SPON1 SH3TC1 AQP3 IRAK3 FAM46A DUSP6 CD1E NCALD TFPI CREG1 SCN3A CD1A GAL3ST4 SLC22A4 BSPRY HSPA1A///HSPA1B CD1B CCR9 CPA3 HOXA5 ADAMTS1 CD1D CTC1 RIPK2 FLT3 CDH2 CD1C PTPRK PVRL2 RAB13 NDST3 AEBP1 SATB1 BHLHE40 CD302///LY75-CD302 RIMS3 E2F2 CD79A PHACTR1 IL1B TBCD GTF2H2B SEC31B KLF9 EFHC2 APBA2 DOCK9 PARP8 FOSL2 SLC35F2 NSG1 DSERG1 TLR5 ARHGEF40 MCTP2 CR2 CDKN2D PLCG1 CEBPD ADAM28 SLC4A7 CD72 IKZF2 HBD PHTF1 ZBTB1 PCDH9 IKZF3 CHRM3 IL18 WDR7 EFNB2 INPP4A RAMP1 GNG11 NMT2 CYP2U1 ETS1 CRYGD MN1 SUV420H1 GPR27 CD28 SYNGR1 HMGA2 WBP1L MAML3 IL10RA WHAMMP2///WHAMMP3 PDGFC KIF3A FRMD4A PLCL1 MECOM SLC27A2 SELPLG RPS11 GRK5 CDC42BPA KLF11 NREP MAP1A CRIP1 CFD ATP8B4 UGP2 CCR8 NETO2 C11orf21 MAP3K8 MATR3///SNHG4 HIVEP3 MBD5 CD69 SLC25A13 HDAC4 BCL2L11 AGBL3 TUBB2A ARMCX1 SRPK2 SLC16A6 PRKCA S1PR1 PAM EPM2AIP1 BIK TMOD2 SERPINE2 F2RL1 ITPR2 DUSP5 CD3E ELK3 GSAP ZNF280D DUSP3 DGKA PDE4D DPPA4 ITGA6 SCN7A TRBC1 EGR1 CXCR7 CCNG2 HOXA3 GRAP2 ID2 NAP1L3 FANCA RAB3D FAM63B MYLIP GATM GOLGA2P5 MYCN FYB SPRY2 ASB9 ARPP21 TNFRSF10B LEF1 LAPTM5 PLA2G4A POLQ ICA1 SEPT9 CD44 IQCJ-SCHIP1///SCHIP1 STS NRIP3 LIG4 TNFRSF1B RAB31 CDKN2C MOB3B SYNE2 KBTBD11 NUDT11 PSRC1 ARSD TCF7 HOXB2 XBP1 DLEU2 PDE4DIP SH2D1A SEMA4C EPCAM NEIL3 UCHL1 LCK CCND2 SLC39A8 ASPM VCAN PVRIG FXYD5 MEST PBK ADM BCL11B GYPC CD63 ATAD2 HS3ST3B1 EVL FHIT SPRY1 APOLD1 ALOX5 CD96 DUSP10 STOM PTCRA RBMS3 RASGRP1 CAPN2 SORL1 TSHR KIF13A IL7R HOPX SERPINB1 RAPGEF5 KCTD15 CD3G TIMP1 SPINK2 KIAA0226L IGHG1 ITK GRB10 MYO1F RAG2 CDC25C CHST2 CKAP4 LYL1 RAG1 NIPAL2 CD2 CTNNA1 CD34 NOTCH3 GLRB ZAP70 TPM1 MPO LRRC1 CD247 SERPINB6 IGF2R IL32 BTK KIAA0922 PCSK5 CLDN10 LOC79015 HOMER1 MYO5C MAL NEDD9 HPGDS ARHGAP19 CHI3L2 MGLL MYBL1 ITGAL NFE2 COX6A1 MGAT4A

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FAH CDC25B LAT ANGPT1 YPEL1 LBH SLC2A5 MME ID3 PYGL AKAP2///PALM2-AKAP2 FAM69A CTBP2 GALNT2 CAMK4 VCL LDLRAD4 UBASH3A IGFBP7 CEP70 ATM SPARC DNTT IPCEF1 DEPTOR ADA BACH2 KIAA0125 MNS1 FAIM3 PRSS2///PRSS3 TFDP2 LRRN3 MYLK FXYD2 TRAF3IP3 PRSS2 SLC4A4 P2RX5 LILRA2 RUFY3 GBP1 LAMC1 GAS7 HTR1F HNRNPR MPL IGHM NLRP3 PALLD MBOAT7 SERHL2 CYB561 TRH WT1 GSTM3 SYNM LAT2 PTPRD MAP7 P2RY2 ERG CD33 GOLIM4 IRF5 TNFAIP2 EIF4EBP1 ID1 FHL1 BCAT1 SLC7A5 FAM49A TCF4 PLEK PLXNB2 LYN MEF2C ALCAM HHEX STAP1 NEK3 ME3 SOCS2 CLEC11A

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Chapter 6 Supplementary Figures

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Supplemental Tables

Table S1: Gene Symbols in each of the 4 gene groups (IV) from Figure 1d

Gene Group // Gene Symbol

1 BIK 2 NSG1 4 RASGRP1

1 CCR9 2 MSRB3 4 PBK

1 KIAA0226L 2 ARRDC4 4 LEF1 1 TFPI 2 MME 4 MAL

1 LATS2 2 EGR1 4 THEMIS

1 LMO2 2 NCALD 4 CD8A

1 B3GNT5 2 RPS11 4 AQP3

1 MAP3K8 2 CAPN2 4 IL32

1 FOSL2 2 LAPTM4B 4 LIG4 1 BHLHE40 2 PRKCA 4 SH2D1A

1 ID2 2 GALNT2 4 ITGAL

1 SPINK2 2 NOTCH3 4 CD2 1 HOPX 2 CR2 4 HHIP

1 DUSP5 2 RHOU 4 SCN3A

1 HHEX 2 NETO2 4 NAP1L3 1 CD69 2 SPRY2 4 CD28

1 GATA3 2 RAB13 4 SPRED2

1 MYLIP 2 GAS2 4 TRBC1 2 NFIL3 2 MEST 4 CRIP1

2 IGFBP7 2 FRMD4A 4 LCK

2 LAT2 2 LOC101060391 4 CHI3L2 2 P2RX5 2 ID3 4 FHL1

2 ZBTB8A 2 ID1 4 FXYD2

2 GBP1 2 IGHG1 4 LOC728613 2 HIVEP3 2 SH3TC1 4 CRNDE

2 NFKBIZ 3 CD1A 4 CLEC11A

2 CCND2 3 RCAN1 4 C11orf96

2 SOCS2 3 CD1E 4 NUDT11

2 NDFIP2 3 CD1B 4 TUBB2A

2 DUSP6 3 OTTHUMG00000017520 /// RP11-144L1.4 4 HSPA1A /// HSPA1B 2 TNFRSF10B 3 DNTT 4 HSPA1A /// HSPA1B

2 TIMP1 3 CEP128 4 PGM2L1

2 BCAT1 3 TSHR 4 BCL2L11 2 NREP 3 FYB 4 NEDD9

2 TCF4 3 TXLNG2P 4 PCDH10

2 FHIT 3 TXLNG2P

2 KCNK17 4 ARHGAP29

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Curriculum Vitae

Rui Daniel Martins Nunes Mendes was born on July 17th, 1984 in Lisbon, Portugal. After finishing high school in Torres Vedras at Escola Secundaria Madeira de Torres, he studied Molecular Biology and Genetics at the University of Lisbon, where he obtained his Bachelor of Science (BSc.) degree in 2007, and Master of Science (MSc.) degree in 2009. He did his master thesis internship “Non-viral gene delivery to human mesenchymal stem cells using cationic liposomes” at the Stem Cell Bioengineering Laboratory at Instituto Superior Tecnico, under the supervision of Dr. Claudia Lobato da Silva. In 2009, he moved to the Netherlands and started working as a researcher for the Orthopedics department at Erasmus Medical Center, Rotterdam. In 2011 he started as a PhD student at the department of Pediatric Oncology/Hematology at Erasmus Medical Center under the supervision of Dr. Jules Meijerink, focusing on the molecular mechanisms that lead to T-cell acute lymphoblastic leukemia. In 2016, he started working as a Scientist for a Biopharmaceutical company in Leiden, ProQR Therapeutics N.V.

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List of publications

Mendes RD, Canté-Barrett K, Pieters R, Meijerink JP. The relevance of PTEN-AKT in relation to NOTCH1-directed treatment strategies in T-cell acute lymphoblastic leukemia. Haematologica. 2016 Sep;101(9):1010-7.

Canté-Barrett K, Mendes RD, Smits WK, van Helsdingen-van Wijk YM, Pieters R, Meijerink JP. Lentiviral gene transfer into human and murine hematopoietic stem cells: size matters. BMC Res Notes. 2016 Jun 16;9:312.

Mendes RD, Sarmento LM, Canté-Barrett K, Zuurbier L, Buijs-Gladdines JG, Póvoa V, Smits WK, Abecasis M, Yunes JA, Sonneveld E, Horstmann MA, Pieters R, Barata JT, Meijerink JP. PTEN micro-deletions in T-cell acute lymphoblastic leukemia are caused by illegitimate RAG- mediated recombination events. Blood, 2014. 124(4): p. 567-78

Madeira C, Mendes RD, Ribeiro SC, Boura JS, Aires-Barros MR, da Silva CL, Cabral JM. Nonviral gene delivery to mesenchymal stem cells using cationic liposomes for gene and cell therapy. J Biom Biotech, 2010; 2010:735349

Ribeiro SC, Mendes RD, Madeira C, Monteiro GA, da Silva CL, Cabral JM. A quantitative method to evaluate mesenchymal stem cell lipofection using real-time PCR. Biotechnol Prog. 2010 Sep-Oct;26(5):1501-4

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PhD Portfolio

Name PhD student: Rui Daniel Martins Nunes Mendes Erasmus MC department: Pediatric Oncology PhD period: 2011-2015 Research School: Postgraduate School Molecular Medicine Promoter: Prof.dr. R. Pieters Co-promoter: Dr. J.P.P. Meijerink

Seminars/Courses/Workshops . Course on Laboratory Animal Science (Article 9) . Basic and Translational Oncology . Analysis of Microarray Gene Expression Data . Basic course on R . Biomedical Scientific English Writing . Indesign workshop . Photoshop & Illustrator workshop . Presenting skills for Scientists . Research Management for PhDs/Postdocs . SNPs and Human Diseases . MolMed Days

Presentations at national and international conferences . Sophia Research Day, Rotterdam, the Netherlands. June 26, 2015, oral presentation . Course on molecular aspects of Hematological Disorders, Rotterdam, the Netherlands. June 9-10, 2015, oral presentation . 1st International Venice Thymus Meeting, San Servolo Island, Italy. April 9-13, 2015, poster presentation . 3rd Daniel den Hoed day, Rotterdam, the Netherlands. February 18, 2015, oral presentation . 19th Congress of the European Hematology Association, Milan, Italy. June 12-15, 2014, poster presentation . Course on molecular aspects of Hematological Disorders, Rotterdam, the Netherlands. June 10-11, 2014, oral presentation . ESH-EHA Scientific Workshop on T-Cell Acute Lymphoblastic Leukemia, Lisbon, Portugal. Mar 22-24, 2013, poster presentation . Erasmus Postgraduate School Molecular Medicine Day, Rotterdam, The Netherlands. Feb 13, 2013, poster presentation . Dutch Hematology Congress, Arnhem, The Netherlands. Jan 23-25, 2013, oral presentation

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Acknowledgements

This is the end of a very important chapter in my life, the last days of my life as a PhD student. So far, the biggest adventure in my life, taking into account the challenge of enrolling a PhD program and living abroad. I could not anticipate what was about to come when I decided to embark in this ship and sail through the unknown, in a journey full of ups-and-downs. At this moment, I can say that every single moment was extremely special, not only for all the people that I met along the way, but also because I had the opportunity to get to know myself even better. Some people were crucial to this achievement and I will never be able to express my words of gratitude your friendship, guidance and support.

My appreciation to my promotor Prof. Dr. Rob Pieters for giving me the opportunity to join the Department of Pediatric Oncology and for all the critical thinking and scientific discussions, especially in guiding us into a patient-oriented research.

To Dr. Jules Meijerink, you were such a great supervisor, and the most important person during the last 5 years of my PhD track. More than a supervisor, you were there every moment that I needed your special support. You always heard my complaints and told me the wisest words of encouragement in the most challenging moments. I truly admire you as an outstanding investigator for your extreme passion and dedication to science, and because of your will and drive to find the answers to the most difficult research questions.

To my colleague (and mentor) Dr. Kirsten Canté-Barrett, you were also extremely important during all these years. You were the one that helped me to start working in most of the projects and whether I needed support in the lab, to discuss ideas or interpret new data, you were you were always eager to help me. I learnt a lot from you and I admire the way you are always open to new ideas and promptly ready to apply them if necessary. Besides that, I truly appreciate your scientific input during our meetings and your management skills, especially how you can efficiently manage so many projects at the same time.

To the whole T-ALL research team, current and former members, you guys made our group a very cohesive team with a great atmosphere to work. Thank you all for you scientific input and for your exceptional support during my PhD track. A very special word to Wilco, Yunlei, Jessica, Elnaz and Eric. Wilco, you were such a great colleague, always eager to help everyone in the 183

lab, enthusiastic to try new ideas and to have a nice scientific discussion. Yunlei, I really had a great time during our collaboration, I just learnt a lot from you and I appreciate all your dedication and patience. Jessica, although we did not have much overlap during our lab activities, I recognize that you are a key in the group and that most of the work and results that I achieved could not be possible without all your commitment since you joined the group. Elnaz, it was very pleasant to share the same office with you, I carry all your words of wisdom with me. Eric, I do appreciate all your advices and that you are so open about your opinions. I will never forget our little adventure when we tried to find something to eat, late in Venice.

To Monique and Ronald, thank you for your great input in my work either directly during our meetings or indirectly through the knowledge and expertise that you created over the years in the department.

To Patricia, I am so happy that I had the opportunity to share the office and have you as a friend. You gave me good advices for my research and for my own career. Besides that it was my pleasure to nurture our friendship along the way. You are a trustworthy friend and a very talented scientist.

To Sandra, you know how your presence was really important to me. Thank you for all your precious support and for being such a great friend.

To all the members of the Pediatric Oncology, my words of gratitude for the scientific discussions, for the support that we always gave to each other, and for all the good moments passed together. In particular, I would like to special thank João, Isabel, Roel, Bob, Mark and Fahrad.

To all the members of the Pediatrics department I want to thank you all for the great environment and for all the events organized. It was a great pleasure to share the labs and having a good time at lunch. My special thanks to Celia and Silvia.

To my new family in Rotterdam: all the Portuguese and international friends that I made during the last years. All the moments we passed together were so important to make me feel at home and learn to be in love with Rotterdam. A special hug to Rodrigo and Leti, Miguel and Lilia, Rui, João, Fernando and Henrique.

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Finally, the last words go to my origins.

To all the friends and family that I have in Portugal and that were always ready to receive me with open arms, warm hugs and love. Those moments passed together were always so special and gave me all the strength to come back and continue my adventure.

To the most important people in my life: my parents and my brother. I honestly don’t have words to say how much I love you! This is not my thesis, this is our thesis. The love that we feel for each other makes us a perfect team that makes impossible things to happen. To my parents, thank you so much for always support me in my decisions and to always encourage me in the most difficult moments, and celebrate with me every little achievement. To my brother, you are just an incredible person, I feel the luckiest guy to have you as my best friend and to have the opportunity to share my whole life with you, thanks for being who you are!

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